Deep Learning for Understanding Satellite Imagery: An Experimental Survey - Mohanty, Sharada Prasanna; Czakon, Jakub; Kaczmarek, Kamil A.; Pyskir, Andrzej; Tarasiewicz, Piotr; Kunwar, Saket; Rohrbach, Janick; Luo, Dave; Prasad, Manjunath; Fleer, Sascha; Göpfert, Jan Philip; Tandon, Akshat; et al. (Frontiers in Artificial Intelligence, 2020)
Translating satellite imagery into maps requires in-tensive effort and time, especially leading to no accurate maps ofthe affected regions during disaster and conflict. The combinationof availability of recent datasets and advances in computervision made through deep learning paved the way towardsautomated satellite image translation. To facilitate research in thisdirection, we introduce the Satellite Imagery Competition usinga modified SpaceNet dataset. Participants had to come up withdifferent segmentation models to detect positions of buildings onsatellite images. In this work, we present 4 approaches based onimprovements of U-net and MaskRCNN models, coupled withunique training tricks using boosting algorithms, morphologicalfilter, CRFs and custom losses. The results as high as AP=0.937and AR=0.959 from these models demonstrate the feasibility ofthe Deep Learning in automated satellite image annotation.