Within the constraints of operational work supporting humanitarian organizations in their response to the Covid‐19 pandemic, we conducted building extraction for Khartoum, Sudan We extracted approximately 1 2 million dwellings and buildings, using a Mask R‐CNN deep learning approach from a Pléiades very high‐resolution satellite image with 0 5 m pixel resolution Starting from an untrained network, we digitized a few hundred samples and iteratively increased the number of samples by validating initial classification results and adding them to the sample collection We were able to strike a balance between the need for timely information and the accuracy of the result by combining the output from three different models, each aiming at distinctive types of buildings, in a post‐processing workflow We obtained a recall of 0 78, precision of 0 77 and F1 score of 0 78, and were able to deliver first results in only 10 days after the initial request The procedure shows the great potential of convolutional neural network frameworks in combination with GIS routines for dwelling extraction even in an operational setting [ABSTRACT FROM AUTHOR] Copyright of Transactions in GIS is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
Mask R‐CNN‐based building extraction from VHR satellite data in operational humanitarian action: An example related to Covid‐19 response in Khartoum, Sudan