A curated list of resources focused on Machine Learning in Geospatial Data Science.
A curated list of resources focused on Machine Learning in Geospatial Data Science.
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
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
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
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
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
Inspired by awesome-tensorflow