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https://github.com/deepVector/geospatial-machine-learning

A curated list of resources focused on Machine Learning in Geospatial Data Science.
https://github.com/deepVector/geospatial-machine-learning

classification computer-vision convolutional-neural-networks deep-learning geoscience geospatial geospatial-machine-learning gis image-segmentation keras landsat machine-learning remote-sensing satellite-imagery satellite-images semantic-segmentation tensorflow

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

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# geospatial-machine-learning



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



## Table of Contents



* [Code projects and Workflows](#code-projects-and-workflows)

* [Datasets](#datasets)

* [Papers](#papers)

* [Books](#books)

* [Courses](#courses)

* [Companies](#companies)



## Code projects and Workflows



* [A 2017 Guide to Semantic Segmentation with Deep Learning](http://blog.qure.ai/notes/semantic-segmentation-deep-learning-review) (2017) by Sasank Chilamkurthy | qure.ai



* [Deeplab Image Semantic Segmentation Network](https://sthalles.github.io/deep_segmentation_network/) (2018) by Thalles Silva | sthalles.github.io



* [deeplab_v3](https://github.com/anxiangSir/deeplab_v3) by anxiangSir | Github



* [deeplab_v3: Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN](https://github.com/sthalles/deeplab_v3) by Thalles Silva | Github



* [Deep learning for satellite imagery via image segmentation](https://blog.deepsense.ai/deep-learning-for-satellite-imagery-via-image-segmentation/) (2017) by Arkadiusz Nowaczynski | deepsense.ai



* [Deep Learning for Semantic Segmentation of Aerial Imagery](https://www.azavea.com/blog/2017/05/30/deep-learning-on-aerial-imagery/) (2017) by Lewis Fishgold and Rob Emanuele | azavea



* [fieldRNN: Temporal Vegetation Classification with Recurrent Neural Networks](https://github.com/TUM-LMF/fieldRNN) by TUM-LMF | Github



* [forecastVeg: A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health](https://github.com/JohnNay/forecastVeg) by John Nay| Github



* [How to do Semantic Segmentation using Deep learning](https://medium.com/nanonets/how-to-do-image-segmentation-using-deep-learning-c673cc5862ef) (2018) by James Le | Medium



* [Kaggle Hackathon with Tensorflow - Satellite Image Classification](https://www.meetup.com/machine-learning-society-sd/events/236876160/) (2017) by Machine Learning Society



* [label-maker: Data Preparation for Satellite Machine Learning](https://github.com/developmentseed/label-maker) by Development Seed | Github



* [Object Detection on SpaceNet](https://medium.com/the-downlinq/object-detection-on-spacenet-5e691961d257) (2016) by Hagerty, P. | Medium



* [Practical advice for analysis of large, complex data sets](http://www.unofficialgoogledatascience.com/2016/10/practical-advice-for-analysis-of-large.html) (2016) by Patrick Riley | The Unofficial Google Data Science Blog



* [Rules of Machine Learning: Best Practices for ML Engineering](https://developers.google.com/machine-learning/rules-of-ml/) (2018) by Martin Zinkevich | Google Developers



* [satellite-image-object-detection: YOLO/YOLOv2 inspired deep network for object detection on satellite images (Tensorflow, Numpy, Pandas)](https://github.com/marcbelmont/satellite-image-object-detection) by Marc Belmont | Github



* [Satellite Image Segmentation: a Workflow with U-Net](https://vooban.com/en/tips-articles-geek-stuff/satellite-image-segmentation-workflow-with-u-net/) (2017) by Chevallier, G. | Vooban



* [semantic_segmentation_satellite_image](https://github.com/msahamed/semantic_segmentation_satellite_image) by Sabber Ahamed | Github



* [ssai-cnn: Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks](https://github.com/mitmul/ssai-cnn) by Shunta Saito | Github



* [raster-vision: deep learning for aerial/satellite imagery](https://github.com/azavea/raster-vision) by azavea | Github



* [Using Convolutional Neural Networks to detect features in satellite images](http://ataspinar.com/2017/12/04/using-convolutional-neural-networks-to-detect-features-in-sattelite-images/) (2017) by Taspinar, A.



* [WaterNet: A convolutional neural network that identifies water in satellite images](https://github.com/treigerm/WaterNet) by Tim Reichelt | Github



## Datasets



- [Dstl Satellite Imagery Feature Detection](https://www.kaggle.com/c/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)](https://www.gov.uk/government/organisations/defence-science-and-technology-laboratory) | Kaggle



- [DeepSat (SAT-6) Airborne Dataset](https://www.kaggle.com/crawford/deepsat-sat6): 405,000 image patches in six land cover classes, by Chris Crawford | Kaggle



- [SAT-4 and SAT-6 airborne datasets](http://csc.lsu.edu/~saikat/deepsat/): Images extracted from the [National Agriculture Imagery Program (NAIP) dataset](http://www.fsa.usda.gov/Internet/FSA_File/naip_2009_info_final.pdf)  by Saikat Basu, Sangram Ganguly, Supratik Mukhopadhyay, Robert Dibiano, Manohar Karki and Ramakrishna Nemani | Louisiana State University



- [SpaceNet](https://registry.opendata.aws/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](http://dx.doi.org/10.1007/s11227-018-2300-2) (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](http://dx.doi.org/10.1117/1.JRS.11.042609) (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](http://dx.doi.org/10.1109/LGRS.2017.2681128) (2017) by Kussul, N., Lavreniuk, M., Skakun, S., Shelestov, A. |  IEEE Geoscience and Remote Sensing Letters



* [Deep learning for visual understanding: A review](http://dx.doi.org/10.1016/j.neucom.2015.09.116) (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](http://dx.doi.org/10.1080/15481603.2017.1323377) 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](cs231n.stanford.edu/reports/2017/pdfs/908.pdf) (2017) by Gardner, D. and Nichols, D. | Stanford University



* [Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models](https://arxiv.org/abs/1708.01580) (2017) by Yao, Y., Liang, H., Li, X., Zhang, J., and He, J. | arXiv



* [TensorFlow: A System for Large-Scale Machine Learning](http://arxiv.org/abs/1605.08695) (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](http://dx.doi.org/10.1007/978-3-319-67349-3) (2018) by Hu, Z., Petoukhov, S., and He, M. | Springer



* [Data processing](http://www.cambridge.org/9780521669481), in *Physical Principles of Remote Sensing* (2001) by Rees, W.G. | Cambridge University Press



* [Deep Learning with Applications Using Python](http://www.apress.com/9781484235157) (2018) by Manaswi, N.K. | Apress



* [Digital Signal Processing and Spectral Analysis for Scientists](http://dx.doi.org/10.1007/978-3-319-25468-5) (2016) by Alessio, S.M. | Springer



* [Hyperspectral Remote Sensing: Fundamentals and Practices](https://www.crcpress.com/9781138747173) (2017) by Pu, R. | CRC Press



* [Image Classification](http://dx.doi.org/10.4135/9780857021052), in *The SAGE Handbook of Remote Sensing* (2009) by Jensen, J.R., Im, J., Hardin, P., and Jensen, R.R. | SAGE Publications



* [Image Processing](http://dx.doi.org/10.1007/978-1-4842-3453-2_4), 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](http://dx.doi.org/10.1002/9781118724194) (2016) by Liu, J.G., and Mason, P.J. | Wiley



* [Mathematical Models for Remote Sensing Image Processing](http://dx.doi.org/10.1007/978-3-319-66330-2) (2018) by Moser, G., and Zerubia, J. | Springer



* [Machine Learning Applications for Earth Observation, Earth Observation Open Science and Innovation](http://dx.doi.org/10.1007/978-3-319-65633-5_8) (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](http://dx.doi.org/10.1007/978-3-319-22560-9) (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](http://dx.doi.org/10.1007/978-1-4842-3096-1) (2017) by Pattanayak, S. | Apress



* [Remote Sensing Digital Image Analysis](http://dx.doi.org/10.1007/978-3-642-30062-2) (2013) by Richards, J.A. | Springer



* [Remotely Sensed Data Characterization, Classification, and Accuracies](https://www.crcpress.com/9781482217865) (2015) by Thenkabail, P.S. | CRC Press



* [Remote Sensing Image Fusion](https://www.crcpress.com/9781466587496) (2015) by Alparone, L., Aiazzi, B., Baronti, S., and Garzelli, A. | CRC



* [Remote Sensing Imagery](http://dx.doi.org/10.1002/9781118899106) (2014) by Tupin, F., Inglada, J., and Nicolas, J.-M. | Wiley



* [TensorFlow Machine Learning Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/tensorflow-machine-learning-cookbook) (2017) by McClure, N. | Packt



## Courses



* [Classification Models](https://www.udacity.com/course/classification-models--ud978) (2018) by alteryx and tab|eau | Udacity



* [Computer Vision Crash Course](https://www.youtube.com/watch?v=-4E2-0sxVUM) (2018) | PBS Digital Studios



* [Deep Learning](https://www.kaggle.com/learn/deep-learning) (2018) by kaggle



* [Intro to Deep Learning](https://www.udacity.com/course/deep-learning--ud730) (2018) by Google | Udacity



* [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) (2018) by kaggle | Udacity



* [Learn TensorFlow and deep learning, without a Ph.D](https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd) (2017) by Görner, M. | Google



* [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) (2018) by Google



* [ML Practicum: Image Classification](https://developers.google.com/machine-learning/practica/image-classification/) (2018) by Google



* [Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/index.html) (2018) by Chip Huyen, Michael Straka, Pedro Garzon, Christopher Manning, Danijar Hafner | Stanford University



## Companies

* [SpaceKnow](https://www.spaceknow.com/)



## Credits

Inspired by [awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow)