Lets map using ‘StreetComplete’ App

Over time the number of OpenStreamMappers is increasing, almost every day the community is getting new Mappers. Indeed it’s a great news but, a matter of tension as well in terms of data quality because of the no or minimal knowledge of tagging, proper drawing, and the fate or end use of data.Its a great news for us that recently a java based android application has been developed what is suitable for new mappers for collecting data in the field offline. It uses Mapzen vector tiles for display. Thanks to Tobias ZwickStreet_Comp.png

Let me introduce you the application named streetcomplete.

  1. Download it from Google Play Store
  2. When you have an internet connection, find the place where you want to map, then you can stop the internet. you can find the area using your current location using mobile data.
  3. You will find the map tile with different features icon. Tap the icon and fill the answers generating against each feature like road, playground, building, path etc.Street_Comp_2.png
  4. After collecting data, upload your answers using the internet.
  5. Dont forget to authorize your OSM account from settings menu of the app.

Enjoy mapping. ~ AHASAN

N.B: The app is aimed at users who do not know anything about OSM tagging schemes but still want to contribute to OpenStreetMap by surveying their neighbourhood (or other places). Because of the target group, the app only presents issues which are answerable very clearly by asking one simple question, and which involve very few false positives.

Original Post 

iD Editor 2.4.0 : Check out the changes of this new release !!!

iD on browser edit panel

The iD editor is the de facto, browser-based OpenStreetMap editor. It was first launched in May 2013 to ease the editing for the osm contributors. iD is fast and easy to use and allows mapping from various data sources such as satellite and aerial imageries, GPS, Field Papers or Mapillary.

The iD editor is a great way to edit for small and easy changes that don’t require the advanced features of JOSM (a more advanced mapping editor). A detailed tutorial on iD editor is given on LearnOSM iD editor Chapter.

iD editor version 2.4.0 has been released on 25 August 2017 having some remarkable features and bug fixes, such as :
? Release Highlights

? A new global imagery layer: Esri World Imagery.  Esri just made their imagery available for OSM use! (Big thaks to ESRI). Check out the new imagery by opening the Background pane (shortcut B)

? New Features

  • Updates to save workflow :
    • Add review_requested changeset tag and checkbox
    • Add source changeset tag and multiselect field
    • Add hashtags changeset tag, API parameter, and auto fill hashtags from comment
    • Write changeset tags for new mappers to indicate walkthrough progress – These tags all start with ideditor:
    • Write changeset tag for changesets_count – it will contain "0" for someone making their first edit
    • Refactor uiCommit into several smaller modules
  • Add addr:unit input to address field for many countries
  • Make rotation and reflection operations available for more geometry types
  • Change raw tag editor readOnlyTags function to accept array of regular expressions
  • name field is no longer automatically added to every preset
  • Field refactor
    • Add options for fields, allow unwrapped fields (no label, buttons, etc)
    • uiField can now be used anywhere, not just inside the preset editor
    • Rename uiPreset -> uiPresetEditor (consistent with raw tag editor, raw member editor, etc)

✨ Usability

  • In save mode, esc should cancel and return to browse mode
  • Recognize more kinds of concrete surface as “paved”
  • When drawing, ignore accidental clicks on mode buttons
  • Change to 80px arrow key panning (this matches Leaflet default)
  • Smoother border around the round vertex preset icon circles
  • Render railway platform slightly different from sidewalk
  • Treat a few special tags as areas even in the absence of a proper area=yes tag.

? Bugfixes

  • Include imagery offset when calculating tiles for background layer
  • Return to browse mode when zooming out beyond edit limit
  • Make sure bool url params actually contain value ‘true’

? Localization

  • Update Chinese address format
  • Swap placement of increment/decrement spin buttons when RTL
  • Fix RTL styling for info panel close buttons
  • Fix RTL styling for spin control and form buttons

⌛️ Performance

  • Use requestIdleCallback in supported browsers for deferred data fetching
    • Avoid reparsing duplicate entities that appear across adjacent OSM tiles
    • Schedule parsing as a low priority task
    • Schedule redraws during idle browser times

? Presets

  • Add signpost term to guidepost preset
  • Remove maxspeed field from living street
  • Make office=physician non-searchable
  • Add preset for amenity=shower
  • Add preset for emergency=life_ring
  • Allow traffic mirror preset on vertex
  • Add presets for many theme park attractions
  • Improve search terms for wetland preset
  • Add jetty search term to amenity=pier preset
  • Remove bin=yes from excrement bag vending machine
  • Improve search terms for group home and social facility presets
  • Allow aerialway station to be drawn as an area
  • Improve search terms for T-bar lift
  • Add hedge preset to barrier category
  • Add railway presets for Derailer, Milestone, Signal, Switch, Train Wash and icons
  • Add railway preset for Buffer Stop, and icon
  • Replace generic “Reference” field with more specific named fields
  • Add preset for Telecom Manhole

The iD map editor is an open source project. You can submit bug reports, help out, or learn more by visiting their project page on GitHub: :octocat: https://github.com/openstreetmap/iD
Finally big thanks to the @TeamID for all their contribution and continuous effors for making more and more usefriendly iD versions.

Source: http://ideditor.com/ and iD Changelog

OpenStreetMap: The Joy of Open Cartography

A nice blog is written under EDUCATIONREMOTE SENSING & GIS section of scientiaplusconsrientia.wordpress.com, can’t wait to share it here.

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I first heard about OpenStreetMap several years ago but did not have enough time or motivation to explore it in depth. That changed last year when I found that I could fit OpenStreetMap data as background maps to a Garmin handheld GPS for use in hiking and bicycle tours (using data downloaded from http://garmin.openstreetmap.nl/). This worked perfectly, and irreversibly awoke my curiosity for this project.

OpenStreetMap is a community-based mapping project on a global scale with the goal of producing and maintaining a complete and updated map that is free from legal and technical restrictions for use. OpenStreetMap implements a Open Data Commons Open Database License (ODbL). That means that one is able to use, modify and reshare the map and data, under the condition of giving credit to OpenStreetMap contributors, sharing under the same terms and keeping the data open. As the database grows in size and quality, the fact that the data are licensed in this manner makes the project an increasingly valuable resource.

OpenStreetMap homepage showing the layers dialogue.
                                                 OpenStreetMap homepage showing the layers dialogue.

In OpenStreetMap, any cartographer (amateur or otherwise) can contribute by mapping new features or editing existing ones. The resulting data can be used directly through its web interface, but it can also be advantageously integrated in websites, GIS platforms, and mobile applications. The potential uses are manifold. OpenStreetMap works for maps in the same way as Wikipedia does for facts. If you like cartography and open data, OpenStreetMap might be a perfect match for you.

Southern suburbs of Buenos Aires, Argentina. Streets and motorways as vectors from OpenStreetMap, superimposed on a Sentinel-2 image. Rendered in QGIS.
Southern suburbs of Buenos Aires, Argentina. Streets and motorways as vectors from OpenStreetMap, superimposed on a Sentinel-2 image. Rendered in QGIS.

There are many things that can be told about how to use OpenStreetMap (OSM) data. We can just begin by the most basic of them, namely how can we start contributing to the map. Before we start, it is worth to mention that the basic idea behind OSM is that the world can be described using features, which are composed of tags attached to basic elements (nodes, ways and relations). It took a little while for me to familiarize myself with this model but it is easily understood after some reading and practice. Also remember that OSM is a community based project. As in any project of this kind, there is an important code of conduct agreed upon by the participants. Please respect it for everyone’s enjoyment and benefit.

After creating a login, we may just start editing using the iD interface integrated in the web page. This is the recommended option for beginners (in contrast to the more advanced standalone application JOSM):

OpenStreetMap home page.
                                                                                    OpenStreetMap home page.

Once logged in, we can then click in the “edit” button, marked in the picture above (the menu appears in Swedish in my case, sorry I could not change it). Once into the editing mode, there is a simplified editing menu, reminiscent of those in GIS tools, with options for adding points, lines or polygons. It also allows to edit the nodes. The background images are from Bing maps:

OpenStreetMap in editing mode (iD).
                                                            OpenStreetMap in editing mode (iD).

Clicking in an object, in this case the coastline of the Island of Capri, Italy, shows a menu to the left with the tags with which the mappers have marked this object. Also note the node editing dialogue next to the selected node. One can then proceed to add, move and delete nodes or features, as well as adding or modifying tags.

OpenStreetMap in editing mode (iD).
                                                                        OpenStreetMap in editing mode (iD).

After saving the edits the newly created features or the edits to existing features are committed to the database. After a while, the servers will show the updated view service in the main map.

This simple recipe is the basis for a lot of the mapping work that goes into making OSM a great tool. Now think of all the unmapped features that may be present in your neighborhood or other areas you know well: hiking paths, bus stops, street names, forests, parks and a long list of etceteras. There are surely many potential improvements that you can easily introduce to the map. Go on and enjoy your mapping!

Chikungunya Map: Areas at Higher Risk in Dhaka

Chikungunya is a mosquito-borne viral disease first described during an outbreak in southern Tanzania in 1952. It is an RNA virus that belongs to the alphavirus genus of the family Togaviridae. The name “chikungunya” derives from a word in the Kimakonde language, meaning “to become contorted”, and describes the stooped appearance of sufferers with joint pain (arthralgia). Recently it has been much evident in Dhaka city like other areas of Bangladesh.

Institute of Epidemiology, Disease Control and Research or IEDCR gave a list of the locations while presenting a research paper at the Secretariat on Thursday.  The areas are Dhanmondi 32,  Sector 4 and Sector 9 of Uttara, Maddhya Badda, Gulshan 1, Lalmatia, Pallabi, Moghbazar, Malibagh Chowdhury Para, Rampura, Tejgaon, Banani, Noyatola, Kuril, Pirerbag, Rayerbazar, Shyamoli, Monipuripara, Mohammadpur, Mohakhali, Mirpur-1 and Korail slum.

Symptoms

  • Most people infected with chikungunya virus will develop some symptoms.
  • Symptoms usually begin 3–7 days after being bitten by an infected mosquito.
  • The most common symptoms are fever and joint pain.
  • Other symptoms may include headache, muscle pain, joint swelling, or rash.
  • Chikungunya disease does not often result in death, but the symptoms can be severe and disabling.
  • Most patients feel better within a week. In some people, the joint pain may persist for months.
  • People at risk for more severe disease include newborns infected around the time of birth, older adults (≥65 years), and people with medical conditions such as high blood pressure, diabetes, or heart disease.
  • Once a person has been infected, he or she is likely to be protected from future infections.

Diagnosis

  • The symptoms of chikungunya are similar to those of dengue and Zika, diseases spread by the same mosquitoes that transmit chikungunya.
  • See your healthcare provider if you develop the symptoms described above and have visited an area where chikungunya is found.
  • If you have recently traveled, tell your healthcare provider when and where you traveled.
  • Your healthcare provider may order blood tests to look for chikungunya or other similar viruses like dengue and Zika.

Treatment

  • There is no vaccine to prevent or medicine to treat chikungunya virus.
  • Treat the symptoms:
    • Get plenty of rest.
    • Drink fluids to prevent dehydration.
    • Take medicine such as acetaminophen (Tylenol®) or paracetamol to reduce fever and pain.
    • Do not take aspirin and other non-steroidal anti-inflammatory drugs (NSAIDS until dengue can be ruled out to reduce the risk of bleeding).
    • If you are taking medicine for another medical condition, talk to your healthcare provider before taking additional medication.
  • If you have chikungunya, prevent mosquito bites for the first week of your illness.
    • During the first week of infection, chikungunya virus can be found in the blood and passed from an infected person to a mosquito through mosquito bites.
    • An infected mosquito can then spread the virus to other people.

Source: WHO; CDC

Cyclone Mora : Probable Impact Areas in Bangladesh

Potential storm surge flooding from Tropical Cyclone Mora in southeast Bangladesh indicated by aqua-colored arrows.  (Google Maps)

According to satellite imagery, Mora’s maximum surface sustained winds were about 83 km/h (52 mph) with gusts to 102 km/h (63 mph). The estimated central pressure was about 992 hPa.

Tropical Cyclone Mora forecast track by RSMC New Delhi May 29, 2017

The system is likely to intensify further into a Severe Cyclonic Storm during the next 12 hours, the center said. It is very likely to move north-northeastwards and cross Bangladesh coast near Chittagong on May 30 (morning, local time).

Bangladesh meteorological department’s special weather bulletin: sl. No. 14 (fourteen), [date: 30.05.2017]

The severe cyclonic storm ‘MORA’ (ecp 990 HPA) over north Bay and adjoining east central Bay moved slightly northwards and lies over the same area (near lat 19.5°n and long 91.3°e) and was centred at midnight last night (the 29 may 2017) about 305 kms south of Chittagong port, 230 kms south of Cox’s bazar port, 380 kms South-Southeast of Mongla port and 300 kms South-Southeast of Payra port. It is likely to intensify further, move in a northerly direction and may cross Chittagong – Cox’s bazar coast by morning of 30 may 2017.

Under the peripheral influence of severe cyclonic storm ‘MORA’ gusty/squally wind with rain/ thunder showers is likely to continue over north bay and the coastal districts and maritime ports of Bangladesh.

Maximum sustained wind speed within 64 kms of the cyclone centre is about 89 kph rising to 117 kph in gusts/squalls. Sea will remain high near the system.

Maritime ports of Chittagong and Cox’s bazar have been advised to keep hoisted great danger signal nubmer ten (r) ten.

Coastal districts of Chittagong, Cox’s bazar, Noakhali, Laxmipur, Feni, Chandpur and their offshore islands and chars will come under danger signal number ten (r) ten.
Maritime ports of Mongla and Payra have been advised to keep hoisted great danger signal nubmer eight (r) eight.

Coastal districts of Bhola, Borguna, Patuakhali, Barisal, Pirozpur, Jhalokathi, Bagherhat, Khulna, Satkhira and their offshore islands and chars will come under danger signal number eight (r) eight.

Mora Impact Area and Shelter Location

Under the influence of the severe cyclonic storm ‘mora’ the low-lying areas of the coastal districts of Cox’s bazar, Chittagong, Noakhali, Laxmipur, Feni, Chandpur, Borguna, Bhola, Patuakhali, Barisal, Pirozpur, Jhalokathi, Bagherhat, Khulna, Satkhira and their offshore islands and chars are likely to be inundated by storm surge of 4-5 feet height above normal astronomical tide.

The coastal districts of Cox’s bazar, Chittagong, Noakhali, Laxmipur, Feni, Chandpur, Borguna, Bhola, Patuakhali, Barisal, Pirozpur, Jhalokathi, Bagherhat, Khulna, Satkhira and their offshore islands and chars are likely to experience wind speed up to 89-117 kph in gusts/ squalls with heavy to very heavy falls during the passage of the severe cyclonic storm.

All fishing boats and trawlers over north bay and deep sea have been advised to remain in shelter till further notice.

 

Forecast Path

Forecast Path : The red-shaded area denotes the potential path of the center of the tropical cyclone. Note that impacts (particularly heavy rain, high surf, coastal flooding) with any tropical cyclone may spread beyond its forecast path.

Torrential rainfall is expected along, north and to the east of the track over eastern Bangladesh, northeast India and western Myanmar, extending northward to the foothills of the Himalayas. This includes the Bangladesh capital of Dhaka, home to over 10 million, one of the world’s most densely populated cities.

This heavy rainfall extending well inland could trigger life-threatening flooding and, in mountainous areas, mudslides.

Rainfall Potential Through Wednesday

Rainfall Potential Through Wednesday

Much heavier rain may occur where rainbands train across the same area for several hours.

Of the 12 tropical cyclones on record that have claimed at least 100,000 lives, eight of those formed in the Bay of Bengal, according to Weather Underground.

One of these, the infamous Great Bhola Cyclone, killed at least 300,000 in November 1970, the world’s deadliest tropical cyclone of record.

In more recent times, Cyclone Nargis in 2008 devasted the Irrawaddy Delta region of Myanmar, claiming at least 130,000 lives.

Less intense storms have also been very deadly in the region.

In 2015, a tropical storm-strength cyclone, Cyclone Komen, hovered near the coast of Bangladesh and brought flooding rain to six countries that killed nearly 500 people. Cyclone Komen made weeks of heavy rainfall even worse as landslides occurred in Myanmar, and more than a million people were evacuated or displaced from Myanmar alone.

 

Source: weather.com; bmd.gov.bd; watchers news

Urban Damage Assessment from High Resolution Images using OSM MASK and Neural Network

A complete methodology has been given by Dariia Gordiiuk of Earth Observatory Systems(EOS) Data Analytics for detecting urban damages Applying Neural Network and Local Laplace Filter and OSM masking Methods to Very High Resolution Satellite Imagery. Hope it will help a lot the geospatial analysts.

Since the beginning of the human species, we have been at the whim of Mother Nature. Her awesome power can destroy vast areas and cause chaos for the inhabitants. The use of satellite data to monitor the Earths surface is becoming more and more essential. Of particular importance are the disasters and hurricane monitoring systems that can help people to identify damage in remote areas, measure the consequences of the events, and estimate the overall damage to a given area. From a computing perspective, such an important task needs to be implemented to assist in various situations.

To analyze and estimate the effects of a disaster, we use high-resolution, satellite imagery from an area of interest. This can be obtained from Google Earth. We can also get free OSM vector data that has a detailed ground truth mask of houses. This is the latest vector zip from New York (Figure 1).

Figure 1. NY Buildings Vector Layer

Next, we rasterize (convert from vector to raster) the image using a tool from gdal, called gdal_rasterize. As a result we have acquired a training and testing dataset from Long Island (Figure 2).

Figure 2. Training Data Fragment of CNN

We apply a deep learning framework Caffe for training purposes and the learning model of Convolutional Neural Networks (CNN):

Figure 3. CNN Parameters

The derived neural net enables us to identify the predicted houses from the target area after the event (Figure 4). We can also use data from another similar area which hasn’t been damaged for CNN learning (if we can’t access the data for the desired territory).

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Figure 4. Predictive Results of CNN Learning

We work with predicates of buildings using vectorization (extracting a contour and then converting lines to polygons) (Figure 5).

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Figure 5. Predictive Results of Buildings (Based on CNN)

Also, we need to compute the intersection of the obtained predicate vector and the original OSM vector (Figure 6). This task can be accomplished by creating a new filter, dividing the square of the predicate buildings by the original OSM vector. Then, we filter the predictive houses by applying a threshold of 10%. This means that if the area of houses in green (Figure 6) is 10% less than the area in red, the real buildings have been destroyed.

 
Figure 6. Calculating CNN-Obtained Building Number (Green) Among Buildings Before Disaster (Red)

Using the 10%-area threshold we can remove the houses that have been destroyed and get a new map that displays existing buildings (Figure 7). By computing the difference between the pre- and post- disaster masks, we obtain a map of the destroyed buildings (Figure 8).

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Figure 7. Buildings: Before and After Disaster With CNN Method
 
Figure 8. Destroyed Buildings With CNN

We have to remember that the roofs of the houses are represented as flat structures in 2D-images. This is an important feature that can also be used to filter input images. A local Laplace filter is a great tool for classifying flat and rough surfaces (Figure 9). The first image has to be a 4-channel image with the fourth Alpha-channel that describes no-data-value pixels in the input image. The second image (img1) is the same, a 3-channel RGB image.

 
Figure 9. Local Laplace Window Filter

Applying this tool lets you get the map of the flat surface. Let’s look at the new mask of the buildings which have flat and rough textures (Figure 10) after combining this filter and extracting the vector map.

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Figure 10. Flat Surface Mask With Laplace Window Filter Followed By Extracted House Mask

A robust library of the OpenCV computer vision has a denoising filter that helps remove noise from the flat buildings masks (Figure 11, 12).

Figure 11. Denoising Filter
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Figure 12. Resulting Mask. Pre- and Post- Disaster Images After Applying Denoising Filter

Next, we apply filters to extract the contours and convert the lines into the polygons. This enables us to get new building recognition results (Figure 13).

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Figure 13. Predictive Results of Buildings With Laplace Filter

We compute the area of an intersection vector mask obtained from the filter and a ground truth OSM mask and use a 14% threshold to reduce false positives (Figure 14).

Figure 14. Calculations: Buildings With Laplace Filter (Yellow) Before Damage (Green), Using 14% Threshold

As a result, we can see a very impressive new mask that describes houses that have survived the hurricane (Figure 15) and a vector of the ruined buildings (Figure 16).

Figure 15. Before and After Disaster With Laplace Filter
 
Figure 16. Destroyed Buildings With Laplace Filter

After we have found the ruined houses, we can also pinpoint their location. For this task OpenStreetMap comes in handy. We have installed an OSM plugin in QGis and added an OSM layer to the canvas (Figure 17). Then, we added a layer with the destroyed houses and we can see all their addresses. If we want to get a file with the full addresses of the destroyed buildings we have to:

  1. In QGis use Vector / OpenStreetMap / Download the data and select the images with the desired information.
  2. Then in QGis use Vector / OpenStreetMap / Import a topology from XML and generate a DataBase from the area of interest.
  3. QGis / Vector / Export the topology to Spatialite and select all the required attributes. (Figure 18)
Figure 17. Destroyed Houses Location
Figure 18. Required Attributes Selection To Load Vector Into Ruined Buildings

As a result, we can get a full list, with addresses, of the destroyed buildings (Figure 19).

Figure 19. Address List of Ruined Houses

If we compare these two different approaches to building recognition, we notice that the CNN-based method has 78% accuracy in detecting destroyed houses, whereas the Laplace filter reaches 96.3% accuracy in recognizing destroyed buildings. As for the recognition of existing buildings, the CNN approach has a 93% accuracy, but the second method has a 97.9 % detection accuracy. So, we can conclude that the flat surface recognition approach is more efficient than the CNN-based method.

The demonstrated method can immediately be very useful and let people compute the extent of damage in a disaster area, including the number of houses destroyed and their locations. This would significantly help while estimating the extent of the damage and provide more precise measurements than currently exist.

Source: Dariia Gordiiuk from Earth Observatory System (EOS) 

Enjoy Tracing Your Area Using Super Resolution DG Image!!!

OpenStreetMap has the several million contributors and obvious that it gonna get higher in near future too. Everyday more and more features are being added allover the world what making OSM more special. Accurate, high-resolution and up-to-date satellite imagery is an essential component for improving this continuously evolving map of our planet – whether it is to trace new features or to use as a reference layer for validation. DigitalGlobe has supported the OSM community for years through partnerships, and today, we are pleased to announce that we will also support OpenStreetMap directly.

Yesterday (9 May 2017), two new global satellite imagery layers are live for tracing on OpenStreetMap, courtesy of DigitalGlobe. Now, mappers have even more sources of high quality, recent imagery layers to trace, identify, and validate roads, places, and buildings to continue to expand this free and open database of the Earth’s features.

OSM contributors will see a new imagery source in addition to imagery being provided by  Bing and Mapbox. You will now see the following two image services from DigitalGlobe:

DigitalGlobe-Premium is a mosaic composed of DigitalGlobe basemap with select regions filled with +Vivid or custom area of interest imagery, 50cm resolution or better, and refreshed more frequently with ongoing updates

DigitalGlobe-Standard is a curated set of imagery covering 86% of the earth’s landmass, with 30-60cm or resolution where available, backfilled by Landsat. Average age is 2.31 years, with some areas updated 2x year.

In JOSM:

  • Go to Imagery> Imagery Preferences
  • Find and select DigitalGlobe Standard  and DigitalGlobe Premium
  • Activate
  • OK

In iD Editor:

  • Zoom to Edit
  • Click on Background Settings
  • Find and select DigitalGlobe Standard  and DigitalGlobe Premium

Please use the attribution: source=DigitalGlobe

DigitalGlobe anticipate questions and feedback about this release. We are addressing them through an active forum with FAQs here. We have a short human readable End User License Agreement to summarize terms for editing OpenStreetMap :

“DigitalGlobe, Inc. is pleased to provide its high resolution satellite imagery to OpenStreetMap in support of its mapping initiatives. By using our imagery in the OSM editor, you understand and agree that you may only use our imagery to trace, and validate edits that must be contributed back to OSM. You cannot download our imagery or use our imagery for any other purpose. We retain all right, title and interest in and to our imagery. We provide our imagery “as is,” with all faults and as available; we disclaim all warranties, express or implied, to the extent permitted by applicable law. You can recover from us only direct damages up to an amount equal to the fees you have paid to us to use our imagery on OSM, if any. We are not liable for any other damages, including consequential, lost profits, special, indirect, incidental or punitive damages.”

Enjoy Mapping !!

Source: blog.digitalglobe.com & Kevin Bullok

Download OpenStreetMap Shapefiles

Open Street Map – is free editable and community driven dynamic spatial data portal supporting to almost all disaster responses, community services and many more use cases. A growing collection of shapefile map downloads by continent, region and country. These maps are community edited and are not always complete. There are various portal what support open street map shapefiles  downloading free for the users:

OSM Boundaries: Different administrative levels from Open Street Map can be downloaded as follows – please note these are community edited, and not always complete:

  • Select a country, then *right-click* the country and click on “select children”.
  • To drill down to further levels, *right-click* on one of the regions (or “children”) displayed, and again click on “select children”.
  • Once you have selected what you need, choose “shp” in the bottom-left corner and click on the “Export” button in the bottom of the screen.

VDSTECH:  A good source of premade shapefules download from Openstreetmap

GEOFABRIK: If you need shapefiles for your GIS applications or processes, look no further. Geofabrik produces shapefiles of all kinds.

OSMCOAST DATA:  OSM collects an amazing amount of geodata and makes it available to the world for free. But the raw OpenStreetMap data is hard to use. On this web site you’ll find some of that data pre-processed and formatted for easier use. Pre-processing includes removing or fixing of wrong data and assembling of different parts of the data into a usable whole. The data is formatted into Shapefiles for easy use in the usual GIS applications.

OSM2SHP: here you can download openstreetmap data in shapefiles format. Data divided by regions: North and South America, Australia and Oceania, Africa, Europa and Asia.61 layers for download. Data filtered by “Map Features” conditions.

BBBike: offers shape files for more than 200 cities and regions worldwide, updated weekly. Separate shapefiles for points, places, waterways, roads, railways, landuse and buildings with relevant tags, then shapefiles with all points, lines and polygons together.

For more please see wiki.openstreetmap.org

Download Free Shapefiles

Shapefile is one of the most common and extensively use vector file format of GIS (Geographical Information System), developed by ESRI as an open Specification, which consist of collection of files viz .shp, .shx and .dbf extension with same prefix name. You might be hunting to Download free shapefile for completing either your small industrial work for POC or for academic project use or for any NGO work.

Thousands of shapefile maps can be downloaded for free here from the following websites, including country shapefiles, shapefiles at province or state level, and other administrative boundaries maps. The shapefile (or ESRI shapefile) format is a geospatial vector format, and is one of the most commonly used map formats.

Physical Geography

General

  • Natural Earth – Vector: Includes coastline, land, oceans, islands, rivers, lakes, glaciated areas and bathymetry. Available at multiple levels of detail. A version of this data is also available in the Wagner VII projection, which has good equal area properties, here.
  • Natural Earth – Raster: Includes various raster images, intended for use as backgrounds for other data, for example hypsometric tints, satellite derived land cover, shaded relief etc.
  • Global Map: A set of consistent GIS layers covering the whole globe at 1km resolution including: transportation, elevation, drainage, vegetation, administrative boundaries, land cover, land use and population centres. Produced by the International Steering Committee on Global Mapping. (Registration Required)
  • DIVA-GIS Country Data: A collection of data collected from a number of the sources below – includes administrative areas, inland water, roads and railways, elevation, land cover, population and climate. Probably the easiest place to get a simple set of data for a specific country.
  • UNEP GEOdata: A wide range of data from the United Nations Environment Programme including Global Forest Cover, Global Potential Evapotranspiration, Global Average Monthly Temperatures, Dams, Watershed Boundaries and much more. To get data, choose Advanced Search and select Geospatial Data Sets from the top drop-down link
  • Koordinates: GIS data aggregation site including data in a number of categories such as elevation, environment, climate etc. Some global datasets, some based on continents, some for specific countries. Mostly vector, but some raster. (Registration Required)
  • MapCruzin: GIS aggregation site including wide range of data for various areas of the world. Some datasets appears to be of low quality, but others are good.
  • GeoNetwork: GIS aggregation site including a wide range of data under various categories (both human and physical).
  • Map Library – Country shapefile maps for Africa in the public domain.
  • StatSilk – Interactive country-level shapefile maps
  • European Environment Agency: Maps and datasets from the European Environment Agency, covering a huge range of physical geography and environmental topics. Europe only.

Land and Ocean Boundaries

  • GSHHS: A Global Self-consistent, Hierarchical, High-resolution Shoreline Database – which basically means it’s good quality (no internal inconsistencies, good accuracy).

Elevation

  • GDEM: 30m resolution global elevation data derived from ASTER satellite images
  • SRTM: Approx 90m (3 arc-second) resolution elevation data from the Shuttle Radar Topography Mission for the whole world.
  • EarthEnv-DEM90: 90m-resolution near-global DEM created by merging the GDEM and SRTM products and post-processing to fill voids and smooth data.
  • ETOPO1: 1 arc-minute resolution relief model including ocean bathymetry.
  • Global Multi-Resolution Topography: Gridded elevation at approximately 100m resolution, covering terrestrial and sea-floor topography.
  • OpenTopography: A community-based site giving free access to high-resolution topography data. Data at the moment appears to be clustered on the West Coast of the USA and in Greenland, and is available both as dense point clouds and processed DEMs.

Weather and Climate

Hydrology

  • HydroSHEDS: Hydrological data and maps based on the STRM elevation data. Includes river networks, watershed boundaries, drainage directions and flow accumulations for the globe.
  • Catchment Characterisation and Modelling: Data on river basins, catchments and rivers for the European Union area.
  • Major Watersheds of the World Deliniation: Vector data showing the outlines of major watersheds (river basins) across the world.
  • Water Isotopes: Global grids of hydrogen and oxygen isotope composition of precipitation and environmental waters in ArcGRID format. Data can be downloaded for whole globe or individual continents.
  • JRC Water Portal: European water data from the EC Joint Research Centre, including data on quantity, quality, price, use, exploitation and irrigation.
  • General Bathymetric Chart of the Oceans: A range of gridded bathymetric datasets compiled by a group of experts.
  • EarthEnv Freshwater Ecosystems Environmental Information: 1km-resolution environmental information for freshwater ecosystems, covering almost the whole globe. Information includes climate, land-cover, soil and geology.

Snow/Ice

Natural Disasters

  • Natural Disaster Hazards: Hazard Frequency, Mortality and Economic Loss Risk as gridded data for the globe. Covers cyclones, drought, earthquakes, flood, landslide, volcano and a combination of them all (‘multihazard’).
  • USGS Earthquakes Database: KML files of all earthquakes recorded by the USGS (across the whole world) from 1973 to present. Available as one dataset or grouped by magnitude or year.
  • Global Seismic Hazard Map: Gridded data showing hazard risk of seismic activity across the globe.
  • IBTrACS: Hurricane and tropical cyclone tracks, including attributes such as minimum pressure, maximum winds. (Also, USA tracks can be visualised here)
  • NOAA/WDC Historical Tsunami Database: Location information of tsunami sources and run-up events, including many attributes (eg. maximum water height, travel time). Available in TSV format which can be imported into GIS systems.
  • MODIS Fire Detection Data: Frequently updated data (including last 7 days of fires) in 1km grid format, derived from thermal anomalies from MODIS data.
  • Lightning and Atmospheric Electricity Dataset: Wide range of data on lightning activity, including average flashes per grid cell per year.
  • NOAA Historical Hurricane Tracks: Hurricane tracks for all North/Central American hurricanes. Data can be exported by clicking the Download button on the top right.
  • Natural Disaster Hotspots: A wide range of geographic data on natural disasters (including volcanoes, earthquakes, landslide, flood and ‘multihazards’) with hazard frequency, economic loss etc.

Land Cover

  • USGS Land Cover Institute: Great set of links to almost all land cover datasets. Links here include most of the datasets below, and many more esoteric data such as river observations, aquifers data and ocean colour information. Although the page starts with US data, it continues with data for other continents lower down the page
  • Corine Land Cover Map: Europe only. Satellite derived land cover, available as at 1990, 2000 and 2006 in vector and raster formats. 100m and 250m resolutions.
  • GLOBCOVER: Global land cover dataset at 300m resolution from the MERIS sensor on the ENVISAT satellite.
  • Climate Change Initiative Land Cover map: Global land cover dataset at 300m resolution, for 1998-2002, 2003-2007, 2008-2012. In many ways the ‘successor’ to GLOBCOVER.
  • MODIS Global Land Cover: 1km and 4km resolution global land cover maps derived from MODIS images.
  • UMD GLC: 1km resolution global land cover maps from the University of Maryland created using a classification tree approach from MODIS data
  • EarthEnv Global Consensus Land Cover: 1km-resolution global land cover, produced by integrating many other land cover datasets, and providing information on the consensus (or lack of consensus) between them
  • Global Land Cover by National Mapping Organisations: 1km data of land cover for the globe, with a classification scheme based on the UN FAO LCCS, facilitating easy comparison with other land cover products.
  • GLC-SHARE: Global Land Cover data combined from ‘best available’ national land cover maps. 1km resolution.
  • Global Lakes and Wetlands Database: Global vector datasets showing areas of lakes, reservoirs, wetlands, swamps, bogs etc.
  • Vegetation Map at Last Glacial Maximum: Broad-scale map of the world showing vegetation cover at the Last Glacial Maximum (25,000 – 15,000 BP)
  • Grassland GIS: Grassland extent data, along with grassland usage (eg. livestock intensity per area of grassland)
  • Forest GIS: Percentage tree-cover, population density and tree cover, share of wood in fuel consumption etc.
  • PALSAR Forest/Non-Forest map: A very detailed (50m resolution) forest map for the whole globe, created from SAR data.
  • Global Forest Change 2000-2014: Loss and gain in tree cover between 2000 and 2014, globally, at 30m resolution.

Ecology

  • Atlas of the Biosphere: Raster maps of environmental variables including soil pH, potential evapotranspiration, average snow depth and many more.
  • Lifemapper: Species distribution maps – both recorded and predicted (based on climate, terrain and land cover). Covers a huge number of species. *Data is available by clicking on Species Archive, searching for a species and then clicking on the number of points and choosing the SHP link at the top. Alternatively, web services are available.
  • WWF World Ecoregions: Shapefile of ecoregions as defined by the WWF Conservation Science Program.
  • Anthropogenic Biomes: Ecosystem unit maps derived from human interactions with ecosystems creating ‘anthropogenic biomes’. Gridded data at 5 arc-seconds available.
  • Amphibian Species Distribution Grids: Approximately 1km resolution gridded data showing species distribution. One file per species.
  • Net Primary Productivity: Gridded Net Primary Productivity data across the globe, including a novel measure of ‘Human Appropriation of NPP’ measuring how much of the NPP of an area humans are using.
  • World Soil Information: Gridded datasets covering the world’s soils at a maximum resolution of 5 arc-minutes with 22 attributes for each cell including organic carbon content, gypsum content, water capacity etc. Data is given for topsoil and subsoil. More detailed datasets for individual countries and continents are available.
  • Harmonized World Soil Database: Combining regional and national soil databases and maps from many countries under the Land Use Change programme of the UN FAO. Includes soil units and parameters (such as pH, depth, and texture) and is at a resolution of 30 arc-seconds.
  • ERS/MetOp Soil Moisture: 25-50km resolution soil moisture data from satellite scatterometer measurements.
  • Global High Resolution Soil Water Balance: Raster data containing actual evapotranspiration and soil water deficit with a resolution of 30 arcseconds (approx 1km).
  • Global Carbon Storage in Soils: Gridded carbon storage in soils for the world, from the World Resources Institute.
  • ReefBase GIS: GIS data about coral reefs worldwide, including extensive attribute data.
  • Human Impacts to Marine Ecosystems: Data from the National Center for Ecological Analysis and Synthesis on human impacts to marine ecosystems. Includes fishing impacts, ocean acidification, sea surface temperature, pollutants and more.
  • Carbon Dioxide Information Analysis Center: All data products from this center are available for download, including atmospheric CO2 concentrations (including the famous Mauna Loa dataset), precipitation data, long-term modelling data and more.
  • UNEP WCMC: Variety of datasets from the United Nations Environment Programme including global wetlands, global distribution of coral reefs, mangrove distributions and more.
  • Aquamaps: Standardised distribution maps for over 11,000 species of fish, marine mammals and invertebrates. *Data available for download under High Resolution Maps and Environmental Data links.
  • Terrestrial Ecoregions of the World: Vector data showing a biogeographic classification of terrestrial ecological regions across the world.
  • Freshwater Ecoregions of the World: Vector data showing a biogeographic classification of freshwater ecological regions across the world.
  • Marine Ecoregions of the World: Vector data showing a biogeographic classification of marine ecological regions across the world.
  • BioFRESH: Contemporary distributions of freshwater species, mapped in vector format across the world
  • Global Habitat Heterogeneity: A set of 14 metrics on the spatial heterogeneity of global habitat, at 1km, 5km and 25km resolutions, derived from MODIS EVI data.

Mineral Resources/Oil and Gas

Human Geography

General

  • UNEP GEOdata: A wide range of data from the United Nations Environment Programme including Nighttime Lights, Pollutant Emissions, Commercial Shipping Activity, Protected Areas and Administrative Boundaries. To get data, choose Advanced Search and select Geospatial Data Sets from the top drop-down link
  • World Bank Geodata: A wide range of World Bank datasets converted to KML format, includes GNP, schooling and financial data.
  • Humanitarian Response Common and Fundamental Operational Datasets Registry: List of freely available datasets for many countries run by the UN Humanitarian Response programme. Contains administrative boundaries, transport, population and more. Fairly empty at the moment but due to be updated soon
  • Atlas of the Biosphere: Gridded human data including per capita oil usage, literacy rate, population growth rate, built-up land and many more.
  • Koordinates: GIS data aggregation site including data in a number of categories such as administrative boundaries, transportation etc. Some global datasets, some based on continents, some for specific countries. Mostly vector, but some raster. Registration required
  • GISTPortal: Wide range of GIS data from a project funded by UNAID to provide spatial data for humanitarian purposes. Registration Required
  • ESPON Grid Data: Various human geography indicators in gridded raster form across Europe, including GDP, population and unemployment in 2003 and 2006.
  • MapCruzin: GIS aggregation site including wide range of data for various areas of the world. Some data appears to be of low quality, but others are good.
  • GeoNetwork: GIS aggregation site including a wide range of data under various categories (both human and physical).
  • Google Maps Gallery: A wide range of user-submitted geographic data, available as a KML file
  • History Database of the Global Environment: Gridded time-series of population, land-use for the last 12,000 years. Also includes GDP, agriculatural areas, yields and greenhous gas emissions for the last century.

Administrative Boundaries

  • Natural Earth: Includes countries, disputed areas, first-order admin (departments, states etc), populated places, urban polygons, parks and protected areas and water boundaries. Available at multiple levels of detail.
  • GADM: Global administrative boundaries, with extensive attribute sets. Covers countries and up to four levels of internal administrative boundary (states, departments, counties etc).
  • World Borders: World country borders with attributes including country codes (FIPS, ISO etc), area and populations.
  • Europe in the World: Administrative boundaries for Europe with lots of attribute data for each country/region including information on economy, demography and infrastructure.
  • CShapes – Historical Boundaries: Historical state boundaries and capitals post-WW2, world-wide, including all changes and dates of changes.
  • VLIZ Maritime Boundaries: Maritime boundaries and areas of Exclusive Economic Zones, including detailed attribute data on treaties etc. From the Flanders Marine Institute.
  • TZ timezones: A map of timezone areas in the world as used in the Unix TZ database format, from which we get the naming Europe/London, America/New_York etc. In shapefile format.

Environmental Boundaries

  • World Spatial Database of Protected Areas: Global vector database of marine and terrestrial protected areas. Rather complicated to download from – instructions at bottom of linked page.
  • IUCN 2013 Red List: Set of shapefiles produced by the IUCN showing the distribution of endangered species of plants and animals across the world
  • Protected Planet: Map of protected areas across the whole world, of almost all types. Available for download by clicking the ‘Download All’ link on the homepage, and then scrolling to the bottom and choosing KMZ, SHP or CSV.

Land Use

  • Global Land Use Dataset: Gridded data at 0.5 degree resolution showing population density, potential natural vegetation, cropland extent, grazing land extent, built-up land extent, crop extent (for 18 major crops) and land suitability for cultivation
  • Human Influence and Footprint: Human Influence Index and Human Footprint calculated from various factors which exert human influence on ecosystems, for example population distribution, urban areas, navigable rivers etc. Available at 30 arc-second resolution.
  • Global Agricultural Lands: Extent and intensity of use of agricultural lands (both cropland and pasture) in 2000 from MODIS and SPOT images and agricultural inventory data.
  • Global Irrigated Area and Rainfed Crops Areas: Vector mapping of global irrigated cropland and rainfed cropland.
  • Crop Calendar GIS: Gridded data on planting dates and harvesting dates across the world for 19 crops. Available at 5 minute and 0.5 degree resolutions.
  • EarthStat: Agricultural Land Use and potential use: A number of GIS datasets on agricultural land use, including global cropland and pasture from 1700 to 2007, harvested areas and yields for 175 crops, and global fertiliser application rates.
  • ESPON Urban Morphological Data: Data on urban areas for Europe including many attributes.
  • European Urban Morphological Zones: Data derived from the CORINE landcover dataset showing all sets of urban areas lying less than 200m apart.

Lakes, Oceans and other Water Sources

  • Coastal Water Quality: Quality of coastal waters across the globe measured by chlorophyll concentrations from SeaWIFS satellite. Data for 1998 and 2007.
  • Global Reservoir and Dam Database: Geographically-referenced data on all reservoirs with a storage capacity of more than 0.1 cubic kilometres. The data consists of polygons outlining reservoirs at high spatial resolution with extensive metadata about the dam and reservoir. Registration required

Wars, Conflict and Crime

  • ACLED: Armed Conflict Location and Event Data – containing all reported conflict events in 50 countries in the developing world. Data from 1997 to present, and in Afghanistan and Pakistan from 2006 until present.
  • Uppsala Conflict Data Programme – Georeferenced Event Database: Locations of instances of political violence in Africa and Asia.
  • Global Terrorism Database: A database of terrorist events (both domestic and international) across the world from 1970-2008, including location and attribute information.
  • Peace Research Institute Oslo: A range of data including armed conflict locations, replication data, arms trade flows and resource datasets.

Population

  • Gridded Population of the World: Includes raw population, population density, both historic, current and predicted.
  • Global Rural-Urban Mapping Project: Based on the above, but includes information on rural and urban population balances.
  • WorldPop: High-resolution, contemporary data on population across Africa, Asia and Central/Southern America. Combines the AfriPop, AmeriPop and AsiaPop projects.
  • Large Urban Areas 1950-2050: Historic, current and future estimates of populations in large urban areas of the world.
  • Global Urban Extent: Maps showing urban extent across the world, at 500m resolution, derived from MODIS images. Requires email to author to download
  • GeoHive: Population and country statistics. Not provided in GIS data formats, but can easily be converted from CSV

Buildings, Roads and Points of Interest

  • OpenStreetMap: Crowd-sourced data for the whole world consisting of most things you’d find on a standard local paper map: points of interest, buildings, roads and road names, ferry routes etc.
  • OSM Metro Extracts: City-sized extracts of the OpenStreetMap dataset, updated weekly for cities across the world
  • POI Factory: Point of Interest files originally designed for use in GPS units, but they can be loaded into a GIS fairly easily. Widely varying quality, and coverage, but includes such things as shop and business locations (eg. all Tesco stores, all McDonald’s restaurants) as well as places of worship, speed cameras etc. Registration is required. To download data in a GIS-ready form choose Garmin CSV format on the download page. The CSV file will contain Latitude and Longitude in WGS-84 co-ordinates, as well as descriptions.
  • SimpleGeo’s Places: Point of Interest data from SimpleGeo, provided as a 2Gb Zip file and licensed under the Creative Commons license. Contains over 21 million POIs for over 63 countries.
  • Nuclear Power Station locations: Locations of all nuclear power stations worldwide (according to the IAEA), provided as a Google Fusion Table. Export to CSV for easy import to a GIS system

Transport and Communications

  • Open Flights: Airport, airline and route data across the globe. Data is provided as CSV files which can be easily processed to produce GIS outputs. Data includes all known airports, and a large number of routes betwen airports.
  • World Port Index: Dataset from the National Geospatial Intelligence Agency listing approximately 3700 ports across the world, with location and facilities offered.
  • Global Roads Open Access Data Set: A vector dataset of roads across the world, using a globally consistent data model, and suitable for mapping at the 1:250,000 level. Only roads between settlements are included, not residential streets, and the dataset is accurate to approximately 50m. This dataset is in beta-testing at the moment and will be fully available shortly
  • Undersea Telecommunications Cables: Open source undersea telecommunication cables map, updated frequently. Data can be visualised in the embedded viewer or shapefiles can be downloaded by clicking the Raw Data link on the top right.
  • Capitaine European Train Stations: Metadata for all train stations in Europe including latitude and longitude.

Gazetteers (place/feature names)

  • NGIS Country Files: A list of names of regions, areas and populated places for each country in the world, provided by the US Government, with geo-references for each place.
  • Geonames Country Information: List of all countries in the world with ISO and ISO3 country code (eg. GB for the United Kingdom and FR for France) with capital city, area, population, internet top-level domain, currency, official languages and neighbours.
  • GRUMP Settlement Points: Locations of individual settlements (as a time series, showing new settlements appearing over time), derived from the Global Rural-Urban Mapping Project.

Miscellaneous

  • G-Econ: Geographically-based economic data, basically providing measurements like GDP but on a raster cell basis (known as Gross Cell Product).
  • Internet Map: Data which can be used to produce maps like those shown here showing major linkages in the internet, as well as density of people online.

Reference:

patch-recepteur.blogspot.com

igismap.com

freegisdata.rtwilson.com

Understand the Shapefile !!!

A shapefile is an Esri vector data storage format for storing the location, shape, and attributes of geographic features. It is stored as a set of related files and contains one feature class. Shapefiles often contain large features with a lot of associated data and historically have been used in GIS desktop applications such as ArcMap. If you have a small amount of data in a shapefile—generally fewer than 1,000 features—you can make it available for others to view through a web browser by adding it as a .zip file containing the .shp, .shx, .dbf, and .prj files to a map you create with the map viewer.

Shapefiles are made up of mandatory and optional files. The mandatory file extensions needed for a shapefile are .shp, .shx and .dbf. The optional files are: .prj, .xml, .sbn and .sbx

If you have several hours to spare, you could go through the  34-page ESRI Shapefile Technical Description document.

Let’s take a closer inspection at what makes up an ArcGIS shapefile.

List of shapefile extensions (Mandatory)
.shp: ESRI file that represents the feature geometry. Each shapefile has it’s own .shp file that can represent points, lines and polygons in a map. Mandatory

.shx: ESRI and AutoCAD shape index position. This type of file is used to search forward and backwards. Mandatory.

.dbf: Standard database file used to store attribute data and object IDs. .dbf can be opened in Microsoft Access or Excel. Mandatory.

Shapefile Extensions (Optional)
.prj: This file type contains the metadata associated with the shapefiles coordinate and projection system. If this file does not exist, the error “unknown coordinate system” will appear. To fix this error, the “define projection” tool generates .prj files. Optional.

.xml: This file type contains the metadata associated with the shapefile. Delete this file, and you essentially delete your metadata. This file type (.xml) can be opened and edited in any text editor. Optional.

.sbn: Spatial index file that optimizes spatial queries. This file type is saved together with a .sbx file. These two files make up a shape index to speed up spatial queries. Optional.

.sbx: Similar to .sbn files, this file type speeds up loading times. It works with .sbn files to optimize spatial queries. We tested .sbn and .sbx extensions and found that there were faster load times when these files existed. It was 6 seconds faster (27.3 sec versus 33.3 sec) comparing with/without .sbn and .sbx files. Optional.

.cpg: These are optional plain text files that describes the encoding applied to create the Shapefile. If your shapefile doesn’t have a cpg file, then it has the system default encoding. Optional.

In Windows Explorer: When moving shapefile files from one folder to another, you should drag and drop all the mandatory and optional files.

In ArcCatalog: When moving shapefiles, it will move all the mandatory and optional files for you.

There are over 150 different GIS file extensions that exist. These file types are exclusively used in GIS. This doesn’t even include AutoCAD and common image formats.

The most common GIS file type are shapefiles. Even the USGS Earth Explorer accepts shapefiles as input to define boundaries.