2020/11/17 by Jayant Gupta, Yiqun Xie, Gupta, Jayant +3 · 1 citation
Computer Science · Environmental Science · Mathematics · #Artificial intelligence #Artificial neural network #Cartography #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Deep learning #Ecology #FOS: Computer and information sciences #Geography #Hardiness (plants) #Land Use and Ecosystem Services #Machine Learning (cs.LG) #Mathematics #Physical geography #Remote Sensing in Agriculture #Spatial ecology #Spatial variability #Statistics #Urban Green Space and Health #cs.CV #cs.LG
paper · pdf · doi:10.48550/arxiv.2011.08992
published in arXiv (Cornell University) (Cornell University) · Accepted in 1st ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems (Deepspatial 2020), San Diego, CA, August 24, 2020
arxiv created 2020/11/17 · openalex publication_date 2020/11/17 · arxiv updated 2020/11/19 · openalex created_date 2022/07/25 · openalex updated_date 2026/08/05
Spatial variability has been observed in many geo-phenomena including\nclimatic zones, USDA plant hardiness zones, and terrestrial habitat types\n(e.g., forest, grasslands, wetlands, and deserts). However, current deep\nlearning methods follow a spatial-one-size-fits-all(OSFA) approach to train\nsingle deep neural network models that do not account for spatial variability.\nIn this work, we propose and investigate a spatial-variability aware deep\nneural network(SVANN) approach, where distinct deep neural network models are\nbuilt for each geographic area. We evaluate this approach using aerial imagery\nfrom two geographic areas for the task of mapping urban gardens. The\nexperimental results show that SVANN provides better performance than OSFA in\nterms of precision, recall,and F1-score to identify urban gardens.\n