Open Urban Mapping — Russia. Hooray! We have recently completed…
Tags: Machine Learning, Openstreetmap, Mapbox, Urban Planning, Earth Observation

Segmentation tackles these challenges tolerably well, but building classification (residential/commercial/etc.), which is an important in our commercial product, will likely perform poor results.

The numbers and the proposed Release plan

To test the data and stay updated, check out our GitHub repository.

We’ll be publishing the data by region, starting with those regions where we surpass the current state of the OSM by count the most (Geoalert (Free) / OSM).

Aggregated statistics for the regions are already there and can be accessed by the reference in the repository.

Top regions of the Russian Federation by the count ratio of buildings (Geoalert / OSM)

Republic of Chechnya

The first place in rating is taken by the Republic of Chechnya, a rather remote and rural region, for which only the capital city of Grozny is mapped in the OSM, while most of the other municipalities only contain administrative boundaries and main roads. Neither is the coverage better for Chechnya in the commercial maps such as Google or 2GIS or Yandex Maps, which normally have the most detailed data for Russia.

The private sector has been actively developed and changed over the past years, therefore it differs by more than two times with our premium based satellite imagery output (220 K vs 490 K).

To see how the building footprints are distributed among the municipalities of Chechnya, we queried OSM for administrative boundaries and managed to find 314 borders out of 360 declared officially. This indicates that most settlements (55%) can be uploaded to the OSM as is, without the risk of data conflicts. Here is a couple of graphs for clarity:

Moscow region

As you can see from the rating above even the well-covered Moscow region comes in the top of it. However the difference between the results obtained from commercial imagery, and those from Mapbox Satellite, is relatively small.

Since the mosaic of Mapbox Sattelite images has the better quality for the Moscow region than for the territories of Chechnya or Tyva, the generated dataset has less missing objects (calculated through Recall) as well as less false positives (calculated through Precision). The predicted building classes are also added (see the class_id attribute).

This dataset contains more than 2.6M features!

Getting statistics within the settlements boundaries (data from OSM), it’s gonig to turn out that approximately 9% (or 900+ settlements) do not contain any building features. Basically, these are “dachas” (small settlements) with the area smaller than 1 sq. km, but among of them there are also 3+ sq. km.

67% contain fewer in OSM than in Geoalert Open Urban Mapping. Count ratio for the total area is 2.8

Data Downloading, Validation and Import — what to do next

All statistics to play with can be found here. You are welcome to copy and reuse them as you wish.

All dataset can be downloaded via the link posted on the project’s Github: https://github.com/Geoalert/urban-mapping

An obvious question that arises when preparing data for imports into OSM is how to avoid data overlap conflicts. The Geoalert platform automatically merges the predicted building footprints with the current OSM data fetching it through Overpass turbo API. At this stage the algorithm compares the predicted building footprints with those presented in OSM for the given area, and if both sufficiently overlap (IoU), it replaces the model output with the one taken from OSM and merges the attributes. Such features have its attribute “is_osm” set to True and should be excluded from the import.

Footprints merged with those in OSM are shown in green (Kaah-Khem, Tyva)

The other question we were asked by the users — how to reduce the data size to upload it into JOSM (stand-alone OSM editor) without slowing down the application.

In the future, we look forward to extracting our data in small batches, but for now the suggested way is to use GDAL or QGIS to clip it by the smaller areas you re going to validate and import.

The OpenStreetMap community has strict rules as to what data can be imported and how it must be imported. To abide by these rules, we have created a page for our project in the OSM Wiki (https://wiki.openstreetmap.org/wiki/Geoalert_Open_Urban_Mapping).

We hope that the OSM community will help us with validation of Open Urban Mapping data according to the established rules.

As a conclusion

To use or not to use automatically generated data is always a trade-off between the desirable quality of the cartographic work and the time devoted to it. By our estimates for some cartographic tasks it can speed up the whole process ten times and more.

As we see the growing number of projects around OSM using or implementing AI assisted mapping — we will see more companies contribute and permit to use more recent and/or better quality imagery for humanitarian response and for filling gaps in the world map which still is far from its completeness.

OSM populated area coverage statistics. Source: https://disaster.ninja/.

More to come. Stay tuned!

References