Challenge participants will be asked to track building construction over time, thereby directly assessing urbanization. Spacenet data-preparation details are here. Early results (demonstrating feasibility) are shown below. The dataset will comprise over 40,000 square kilometers of imagery and exhaustive polygon labels of building footprints in the imagery, totaling over 10 million individual annotations. Building Detection in the Spacenet Satellite Imagery Dataset using Single Shot MultiBox Detector (SSD) This project applies the Caffe-based Single-Shot Detector algorithm to the Spacenet dataset. The competition centers around a new open source dataset of Planet satellite imagery mosaics, which includes 24 images (one per month) covering ~100 unique geographies. ![]() SpaceNet: A Remote Sensing Dataset and Challenge Series. In this challenge, participants will identify and track buildings in satellite imagery time series collected over rapidly urbanizing areas. Satellite Imagery Multiscale Rapid Detection with Windowed Networks, Van Etten, A., WACV 2019. The SpaceNet 7 Multi-Temporal Urban Development Challenge aims to help address this deficit and develop novel computer vision methods for non-video time series data. For example, quantifying population statistics is fundamental to 67 of the 232 United Nations Sustainable Development Goals, but the World Bank estimates that more than 100 countries currently lack effective Civil Registration systems. SpaceNet Challenge Datasets have a combination of very high resolution satellite imagery and high quality corresponding labels for foundational mapping. Satellite imagery analytics have numerous human development and disaster response applications, particularly when time series methods are involved. We evaluate our method using two sources of labels (OpenStreetMap, and those from the SpaceNet dataset), and find that models both trained and tested on.
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