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Weir, N., Lindenbaum, D., Bastidas, A., Etten, A., Kumar, V., Mcpherson, S., Shermeyer, J. and Tang, H. (2019) Spacenet MVOI: A Multi-View Overhead Imagery Dataset. Proceedings of the IEEE/CVF International Conference on Computer Vision, Seoul, South Korea, 27 October 2019 - 02 November 2019, 992-1001.

has been cited by the following article:

• TITLE: Building Detection and Counting in Convoluted Areas Using Multiclass Datasets with Unmanned Aerial Vehicles (UAVs) Imagery

  AUTHORS: Shital Adhikari, Vaghawan Prasad Ojha

  KEYWORDS: Multi-Class Segmentation, Building Segmentation, Remote Sensing, Semantic Segmentation, UNet

  JOURNAL NAME: Advances in Remote Sensing, Vol.12 No.3, August 21, 2023

  ABSTRACT: This paper studies the effect of breaking single-class building data into multi-class building data for semantic segmentation under end-to-end architecture such as UNet, UNet++, DeepLabV3, and DeepLabv3+. Although, the already existing semantic segmentation methods for building detection work on the imagery of developed world, where the buildings are highly structured and there is a clearly distinguishable space present between the building instances, the same methods do not work as effectively on the developing world where there is often no clear differentiable spaces between instances of building thus reducing the number of detected instances. Hence as a noble approach, we have added building contours as new class along with building segmentation data, and detected the building contours and the inner building regions, hence giving the precise number of buildings existing in the input imagery especially in the convoluted areas where the boundary between the buildings are often hard to determine even for human eyes. Breaking down the building data into multi-class data increased the building detection precision and recall. This is useful in building detection where building instances are convoluted and are difficult for bare instance segmentation to detect all the instances.