README.md

geospatial-machine-learning

A curated list of resources focused on Machine Learning in Geospatial Data Science.

Table of Contents

• Code projects and Workflows
• Datasets
• Papers
• Books
• Courses
• Companies

Code projects and Workflows

• A 2017 Guide to Semantic Segmentation with Deep Learning (2017) by Sasank Chilamkurthy | qure.ai

• Deeplab Image Semantic Segmentation Network (2018) by Thalles Silva | sthalles.github.io

• deeplab_v3 by anxiangSir | Github

• deeplab_v3: Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN by Thalles Silva | Github

• Deep learning for satellite imagery via image segmentation (2017) by Arkadiusz Nowaczynski | deepsense.ai

• Deep Learning for Semantic Segmentation of Aerial Imagery (2017) by Lewis Fishgold and Rob Emanuele | azavea

• fieldRNN: Temporal Vegetation Classification with Recurrent Neural Networks by TUM-LMF | Github

• forecastVeg: A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health by John Nay| Github

• How to do Semantic Segmentation using Deep learning (2018) by James Le | Medium

• Kaggle Hackathon with Tensorflow - Satellite Image Classification (2017) by Machine Learning Society

• label-maker: Data Preparation for Satellite Machine Learning by Development Seed | Github

• Object Detection on SpaceNet (2016) by Hagerty, P. | Medium

• Practical advice for analysis of large, complex data sets (2016) by Patrick Riley | The Unofficial Google Data Science Blog

• Rules of Machine Learning: Best Practices for ML Engineering (2018) by Martin Zinkevich | Google Developers

• satellite-image-object-detection: YOLO/YOLOv2 inspired deep network for object detection on satellite images (Tensorflow, Numpy, Pandas) by Marc Belmont | Github

• Satellite Image Segmentation: a Workflow with U-Net (2017) by Chevallier, G. | Vooban

• semantic_segmentation_satellite_image by Sabber Ahamed | Github

• ssai-cnn: Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks by Shunta Saito | Github

• raster-vision: deep learning for aerial/satellite imagery by azavea | Github

• Using Convolutional Neural Networks to detect features in satellite images (2017) by Taspinar, A.

• WaterNet: A convolutional neural network that identifies water in satellite images by Tim Reichelt | Github

Datasets

• Dstl Satellite Imagery Feature Detection: A set of 1km x 1km satellite images in both 3-band and 16-band formats, by the Defence Science and Technology Laboratory (Dstl) | Kaggle

• DeepSat (SAT-6) Airborne Dataset: 405,000 image patches in six land cover classes, by Chris Crawford | Kaggle

• SAT-4 and SAT-6 airborne datasets: Images extracted from the National Agriculture Imagery Program (NAIP) dataset by Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert Dibiano, Manohar Karki and Ramakrishna Nemani | Louisiana State University

• SpaceNet: A corpus of commercial satellite imagery and labeled training data to foster innovation in the development of computer vision algorithms | AWS

Papers

• Caffe CNN-based classification of hyperspectral images on GPU (2018) by Garea, A.S., Heras, D.B., and Argüello, F. | The Journal of Supercomputing, p. 1-13

• Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community (2017) by Ball, J.E., Anderson, D.T., and Chan, C.S. | Journal of Applied Remote Sensing, v. 11, p. 54

• Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (2017) by Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. | IEEE Geoscience and Remote Sensing Letters

• Deep learning for visual understanding: A review (2016) by Guo, Y., Liu, Y., Oerlemans, A., Lao, S., Wu, S., and Lew, M.S. | Neurocomputing, v. 187, p. 27-48

• Deep learning in remote sensing scene classification: a data augmentation enhanced convolutional neural network framework by Xingrui Yu, Xiaomin Wu, Chunbo Luo & Peng Ren | GIScience & Remote Sensing 54:5, 741-758

• Multi-label Classification of Satellite Images with Deep Learning (2017) by Gardner, D. and Nichols, D. | Stanford University

• Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models (2017) by Yao, Y., Liang, H., Li, X., Zhang, J., and He, J. | arXiv

• TensorFlow: A System for Large-Scale Machine Learning (2016) by Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D.G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zheng, X. | arXiv

Books

• Advances in Artificial Systems for Medicine and Education (2018) by Hu, Z., Petoukhov, S., and He, M. | Springer

• Data processing, in Physical Principles of Remote Sensing (2001) by Rees, W.G. | Cambridge University Press

• Deep Learning with Applications Using Python (2018) by Manaswi, N.K. | Apress

• Digital Signal Processing and Spectral Analysis for Scientists (2016) by Alessio, S.M. | Springer

• Hyperspectral Remote Sensing: Fundamentals and Practices (2017) by Pu, R. | CRC Press

• Image Classification, in The SAGE Handbook of Remote Sensing (2009) by Jensen, J.R., Im, J., Hardin, P., and Jensen, R.R. | SAGE Publications

• Image Processing, in Introduction to Deep Learning Business Applications for Developers (2018)by Vieira, A., and Ribeiro, B. | Apress

• Image Processing and GIS for Remote Sensing: Techniques and Applications (2016) by Liu, J.G., and Mason, P.J. | Wiley

• Mathematical Models for Remote Sensing Image Processing (2018) by Moser, G., and Zerubia, J. | Springer

• Machine Learning Applications for Earth Observation, Earth Observation Open Science and Innovation (2018) by Lary, D.J., Zewdie, G.K., Liu, X., Wu, D., Levetin, E., Allee, R.J., Malakar, N., Walker, A., Mussa, H., Mannino, A., and Aurin, D. | Springer

• Principles of Applied Remote Sensing (2016) by Khorram, S., van der Wiele, C.F., Koch, F.H., Nelson, S.A.C., and Potts, M.D. | Springer

• Pro Deep Learning with TensorFlow (2017) by Pattanayak, S. | Apress

• Remote Sensing Digital Image Analysis (2013) by Richards, J.A. | Springer

• Remotely Sensed Data Characterization, Classification, and Accuracies (2015) by Thenkabail, P.S. | CRC Press

• Remote Sensing Image Fusion (2015) by Alparone, L., Aiazzi, B., Baronti, S., and Garzelli, A. | CRC

• Remote Sensing Imagery (2014) by Tupin, F., Inglada, J., and Nicolas, J.-M. | Wiley

• TensorFlow Machine Learning Cookbook (2017) by McClure, N. | Packt

Courses

• Classification Models (2018) by alteryx and tab|eau | Udacity

• Computer Vision Crash Course (2018) | PBS Digital Studios

• Deep Learning (2018) by kaggle

• Intro to Deep Learning (2018) by Google | Udacity

• Intro to Machine Learning (2018) by kaggle | Udacity

• Learn TensorFlow and deep learning, without a Ph.D (2017) by Görner, M. | Google

• Machine Learning Crash Course with TensorFlow APIs (2018) by Google

• ML Practicum: Image Classification (2018) by Google

• Tensorflow for Deep Learning Research (2018) by Chip Huyen, Michael Straka, Pedro Garzon, Christopher Manning, Danijar Hafner | Stanford University

Companies

• SpaceKnow

Credits

Inspired by awesome-tensorflow