Challenges of huge open-source datasets for constructing detection in Africa Intermezzo: constructing footprint vs rooftop Comparison Discussion Conclusion References

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Fig 1. Examples of various acquisition conditions for a similar location for Maxar WorldView imagery (WorldView © 2020 MAXAR Technologies). The examples show differences in sun azimuth and elevation angle, and satellite viewing angle, which affect how buildings are depicted and the shadows they forged. ML models might struggle to take care of such variations in appearances.
  • geological and vegetation features that will be confused for built-up structures;
  • areas characterised by small buildings, which might appear only just a few pixels wide at this resolution;
  • buildings constructed with natural materials that are inclined to mix in with the encompassing rural or desert areas;
  • clusters of buildings which can be very close together is probably not easily identified.

A general definition for constructing footprint is a polygon, or set of polygons, representing a selected constructing within the physical world, providing a ground-centred representation of a constructing’s location, shape, dimensions, and area [10]. Getting all this information from overhead satellite imagery won’t be possible, so often algorithms provide an approximation of the footprint, depending on the image acquisition conditions and shape of the constructing. As an illustration, for a few of the high-rise buildings shown here, shadows and orientation of the constructing occlude the actual constructing footprint. In other cases, like for terraced houses or blocks, the separation of constructing footprints doesn’t correspond to physical visible features. Because of this, some automated algorithms are more successful in detecting and delineating constructing rooftops somewhat than the actual footprint.

For a good comparison, we visualise the datasets on the corresponding satellite imagery used to infer the constructing footprints. Comparison across different satellite imagery is difficult because of differences of the image acquisition conditions and processing. Nonetheless, despite the challenges, the estimated constructing footprints should provide a reliable estimate of the particular constructing position whatever the image it was derived from.

Fig 2. Bounding box color and underlying satellite imagery source for every dataset.

What first catches the attention is how the constructing footprints obtained through the RAMP prediction model have a distinctively amorphous shape, lacking well-defined edges, which consequently doesn’t accurately represent the bottom truth. One other limitation of RAMP is the unfinished extraction of larger buildings, in addition to the shortcoming to totally encompass the visible structure depicted within the satellite imagery, visible in some constructing footprints. That is after all a limitation of the model and never of the imagery, and it is probably going because of a scarcity of generalization to latest areas. One issue observed for GOB in all locations is because of partial buildings being predicted, probably because of the stitching of satellite tiles from different acquisition takes.

Fig 3. Example location from Serrekunda, Gambia. Images show the considered open-source dataset overlaid onto the imagery used for his or her inference for a fairer comparison. Top-left, MBF dataset shown in red on Bing Maps imagery. Top-right, GOB dataset shown in green on Google Earth imagery. Bottom-left, RAMP detection shown in yellow on Airbus Pleiades imagery. Bottom-right, MBF, GOB and RAMP datasets shown together on Airbus Pleiades imagery from 2021 (© CNES 2021, Distribution AIRBUS DS).

Areas featuring tall buildings generally exhibit the next accuracy because of their orderly designs. Such scenario is observable in districts in Cairo. Nonetheless, despite the homogeneity of structures, certain constructing footprints within the GOB dataset are fragmented. This will be attributed to the presence of smaller constructions situated on the rooftops of high-rise buildings. Such structures have diverse reflection characteristics and ranging roof heights, resulting in their identification as individual buildings. Quite the opposite, MBF groups high-rise buildings together in the identical bounding box, despite their clear separation. The GOB dataset can be subject to a challenge with accurately representing high-rise buildings, on this specific location their bounding boxes exhibit variation of their delineation. Specifically, certain bounding boxes capture the outline of the constructing’s roof, while others delineate the outline of the structure on the bottom.

Fig 4. Example location from Cairo, Egypt. Images show the considered open-source dataset overlaid onto the imagery used for his or her inference for a fairer comparison. Top-left, MBF dataset in red shown on Bing Maps imagery. Top-right, GOB dataset in green shown on Google Earth imagery. Bottom-left, RAMP detection shown in yellow on Airbus Pleiades imagery. Bottom-right, MBF, GOB and RAMP datasets shown together on Airbus Pleiades imagery from 2021 (© CNES 2021, Distribution AIRBUS DS).

Quickly changing scenery will not be a rare occurrence in Africa, subsequently datasets and satellite imagery don’t all the time reflect current state of the realm of interest. That is highlighted by the observed differences in detections and temporal diversity of underlying satellite imagery in the next comparison. As previously mentioned, it’s unclear which specific dates the MBF and GOB consult with, which might create difficulties in utilizing these two datasets. A notable issue is that the model fails to detect quite a few objects, including each smaller objects within the north and bigger objects within the south of the placement of interest. This presents a challenge to the accuracy and reliability of the model.

Fig 5. Example location from Gatumba, Bujumbura. Images show the considered open-source dataset overlaid onto the imagery used for his or her inference for a fairer comparison. Top-left, MBF dataset in red shown on Bing Maps imagery. Top-right, GOB dataset in green shown on Google Earth imagery. Bottom-left, RAMP detection shown in yellow on Airbus Pleiades imagery. Bottom-right, MBF, GOB and RAMP datasets shown together on Airbus Pleiades imagery from 2021 (© CNES 2021, Distribution AIRBUS DS). In this instance, large difference of land cover will be seen between images, making it difficult to evaluate the temporal veracity.

The restrictions of outdated datasets and inaccuracies in constructing detection models are clearly evident on this particular location of interest. The MBF dataset only includes buildings that were present before the 12 months 2017, while the RAMP prediction model shows significant inaccuracies in detecting buildings on this location, with numerous buildings going undetected and several other false detections of larger size.

Fig 6. Example location from Modderspruit, South Africa. Images show the considered open-source dataset overlaid onto the imagery used for his or her inference for a fairer comparison. Top-left, MBF dataset in red shown on Bing Maps imagery. Top-right, GOB dataset in green shown on Google Earth imagery. Bottom-left, RAMP detection shown in yellow on Airbus Pleiades imagery. Bottom-right, MBF, GOB and RAMP datasets shown together on Airbus Pleiades imagery from 2021 (© CNES 2021, Distribution AIRBUS DS).

In Dakar, we chosen an urban area of interest, where buildings are densely positioned in close proximity to at least one one other. Upon comparing datasets, now we have observed that HIECTORs detections have been essentially the most comprehensive. Nonetheless, there continues to be much room for improvement, as a few of the bounding boxes overlap and there are some false detections, reminiscent of parking spaces and random sections of roads. RAMP prediction model was largely unsuccessful in extracting individual constructing footprints. Many of the detected footprints contain multiple buildings, which poses a big challenge for accurate evaluation and evaluation of the dataset. The MBF dataset also presents a comparable challenge, albeit with fewer such instances observed. As well as, its foremost drawback is that loads of buildings weren’t detected. Analysing the GOB has proven to be difficult because of a unique viewing angle of the underlying satellite imagery. Nonetheless, high frequency of smaller-sized detections stays a persistent issue.

Fig 7. Example location from Dakar, Senegal. Images show the considered open-source dataset overlaid onto the imagery used for his or her inference for a fairer comparison. Top-left, MBF dataset in red shown on Bing Maps imagery. Top-right, GOB dataset in green shown on Google Earth imagery. Bottom-left, RAMP detection shown in yellow on Airbus Pleiades imagery. Bottom-right, HIECTOR predictions shown in blue on Airbus Pleiades imagery from 2021 (© CNES 2021, Distribution AIRBUS DS).

The above review was carried out with the aim of reviewing open-source constructing footprint datasets for his or her use as training dataset over large AOI, i.e., Africa, for our own constructing detection model HIECTOR. The presented quality assessment won’t apply in other use-cases, for example for a rough estimation of buildings in a given area. Nonetheless, for our use-case, we feel like providing the next suggestions and warnings:

  • Consider upfront which satellite imagery will probably be used as base layer, and bear in mind concerning the variations brought in by different acquisition conditions, particularly for those who plan to make use of multiple sources of images.
  • Manually labelled or validated constructing footprints provide essentially the most accurate estimation of constructing footprints, although their spatial coverage could be very limited. Ensure to ascertain the open-source datasets for manually labelled data.
  • For those who goal large areas and manually labelled footprints aren’t an option, consider machine-generated datasets. Nonetheless, the accuracy and coverage of machine-generated constructing footprints greatly varies across regions, so be certain to judge their accuracy using the goal imagery of selection.
  • Although machine-generated datasets won’t be accurate enough for use as training labels, they could present place to begin to hurry up manual labelling and validation. This, again, depends upon the region and on the complexity of the buildings and landscape being depicted.

Accurate and up-to-date constructing footprint data is crucial for various practical and scientific purposes. Latest technologies have made it possible to routinely delineate buildings. Nonetheless, limitations of the input imagery and reference labels still pose challenges, particularly in developing areas where accurate data could also be scarce. To deal with this issue, we explored various open-source datasets available for Africa. We identified a few of the cons and showed that the standard of the datasets varies from location to location and imagine it is vitally necessary to judge the suitability and limitations of those datasets for specific regions and applications. Further efforts are needed to enhance the accuracy and coverage of such datasets, but nevertheless, they supply a promising path towards more accurate and comprehensive constructing footprint data, especially for regions where alternative data sources is probably not available.

[1] https://www.microsoft.com/en-us/maps/building-footprints

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