Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning.
Published in MDPI Remote Sensing, 2021
In this paper, we train deep learning neural networks using TensorFlow to predict impervious surfaces from Landsat 8 images. We used OpenStreetMap (OSM), a crowd-sourced map of the world with manually interpreted impervious surfaces such as roads and buildings, to programmatically generate large amounts of training and evaluation data, thus overcoming the need for manual labeling. We conducted extensive experimentation to compare the performance of different deep learning neural network architectures, optimization methods, and the set of features used to train the networks.
Recommended citation: Parekh, Jash R., Ate Poortinga, Biplov Bhandari, Timothy Mayer, David Saah, and Farrukh Chishtie. "Automatic Detection of Impervious Surfaces from Remotely Sensed Data Using Deep Learning." Remote Sensing 13, no. 16 (2021): 3166.
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