{"id":13566363,"url":"https://github.com/deepVector/geospatial-machine-learning","last_synced_at":"2025-04-04T00:30:36.148Z","repository":{"id":41413385,"uuid":"134464698","full_name":"deepVector/geospatial-machine-learning","owner":"deepVector","description":"A curated list of resources focused on Machine Learning in Geospatial Data Science.","archived":false,"fork":false,"pushed_at":"2018-06-21T20:04:07.000Z","size":93,"stargazers_count":536,"open_issues_count":1,"forks_count":141,"subscribers_count":40,"default_branch":"master","last_synced_at":"2024-11-04T20:42:26.870Z","etag":null,"topics":["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"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/deepVector.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-05-22T19:19:18.000Z","updated_at":"2024-10-30T06:08:57.000Z","dependencies_parsed_at":"2022-08-10T02:07:34.055Z","dependency_job_id":null,"html_url":"https://github.com/deepVector/geospatial-machine-learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepVector%2Fgeospatial-machine-learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepVector%2Fgeospatial-machine-learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepVector%2Fgeospatial-machine-learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/deepVector%2Fgeospatial-machine-learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/deepVector","download_url":"https://codeload.github.com/deepVector/geospatial-machine-learning/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247103305,"owners_count":20884023,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-08-01T13:02:08.013Z","updated_at":"2025-04-04T00:30:35.847Z","avatar_url":"https://github.com/deepVector.png","language":null,"funding_links":[],"categories":["Others","Deep learning and Machine Learning"],"sub_categories":["Testing your code"],"readme":"![](./img/deepVector_GeospatialMachineLearning_2018_banner02_100.png)\n# geospatial-machine-learning\n\nA curated list of resources focused on Machine Learning in Geospatial Data Science.\n\n\n\n## Table of Contents\n\n* [Code projects and Workflows](#code-projects-and-workflows)\n* [Datasets](#datasets)\n* [Papers](#papers)\n* [Books](#books)\n* [Courses](#courses)\n* [Companies](#companies)\n\n## Code projects and Workflows\n\n* [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\n\n* [Deeplab Image Semantic Segmentation Network](https://sthalles.github.io/deep_segmentation_network/) (2018) by Thalles Silva | sthalles.github.io\n\n* [deeplab_v3](https://github.com/anxiangSir/deeplab_v3) by anxiangSir | Github\n\n* [deeplab_v3: Tensorflow Implementation of the Semantic Segmentation DeepLab_V3 CNN](https://github.com/sthalles/deeplab_v3) by Thalles Silva | Github\n\n* [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\n\n* [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\n\n* [fieldRNN: Temporal Vegetation Classification with Recurrent Neural Networks](https://github.com/TUM-LMF/fieldRNN) by TUM-LMF | Github\n\n* [forecastVeg: A Machine Learning Approach to Forecasting Remotely Sensed Vegetation Health](https://github.com/JohnNay/forecastVeg) by John Nay| Github\n\n* [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\n\n* [Kaggle Hackathon with Tensorflow - Satellite Image Classification](https://www.meetup.com/machine-learning-society-sd/events/236876160/) (2017) by Machine Learning Society\n\n* [label-maker: Data Preparation for Satellite Machine Learning](https://github.com/developmentseed/label-maker) by Development Seed | Github\n\n* [Object Detection on SpaceNet](https://medium.com/the-downlinq/object-detection-on-spacenet-5e691961d257) (2016) by Hagerty, P. | Medium\n\n* [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\n\n* [Rules of Machine Learning: Best Practices for ML Engineering](https://developers.google.com/machine-learning/rules-of-ml/) (2018) by Martin Zinkevich | Google Developers\n\n* [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\n\n* [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\n\n* [semantic_segmentation_satellite_image](https://github.com/msahamed/semantic_segmentation_satellite_image) by Sabber Ahamed | Github\n\n* [ssai-cnn: Semantic Segmentation for Aerial / Satellite Images with Convolutional Neural Networks](https://github.com/mitmul/ssai-cnn) by Shunta Saito | Github\n\n* [raster-vision: deep learning for aerial/satellite imagery](https://github.com/azavea/raster-vision) by azavea | Github\n\n* [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.\n\n* [WaterNet: A convolutional neural network that identifies water in satellite images](https://github.com/treigerm/WaterNet) by Tim Reichelt | Github\n\n## Datasets\n\n- [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\n\n- [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\n\n- [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\n\n- [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\n\n\n## Papers\n\n* [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\n\n* [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\n\n* [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\n\n* [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\n\n* [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 \u0026 Peng Ren | GIScience \u0026 Remote Sensing 54:5, 741-758\n\n* [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\n\n* [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\n\n* [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\n\n## Books\n\n* [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\n\n* [Data processing](http://www.cambridge.org/9780521669481), in *Physical Principles of Remote Sensing* (2001) by Rees, W.G. | Cambridge University Press\n\n* [Deep Learning with Applications Using Python](http://www.apress.com/9781484235157) (2018) by Manaswi, N.K. | Apress\n\n* [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\n\n* [Hyperspectral Remote Sensing: Fundamentals and Practices](https://www.crcpress.com/9781138747173) (2017) by Pu, R. | CRC Press\n\n* [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\n\n* [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\n\n* [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\n\n* [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\n\n* [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\n\n* [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\n\n* [Pro Deep Learning with TensorFlow](http://dx.doi.org/10.1007/978-1-4842-3096-1) (2017) by Pattanayak, S. | Apress\n\n* [Remote Sensing Digital Image Analysis](http://dx.doi.org/10.1007/978-3-642-30062-2) (2013) by Richards, J.A. | Springer\n\n* [Remotely Sensed Data Characterization, Classification, and Accuracies](https://www.crcpress.com/9781482217865) (2015) by Thenkabail, P.S. | CRC Press\n\n* [Remote Sensing Image Fusion](https://www.crcpress.com/9781466587496) (2015) by Alparone, L., Aiazzi, B., Baronti, S., and Garzelli, A. | CRC\n\n* [Remote Sensing Imagery](http://dx.doi.org/10.1002/9781118899106) (2014) by Tupin, F., Inglada, J., and Nicolas, J.-M. | Wiley\n\n* [TensorFlow Machine Learning Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/tensorflow-machine-learning-cookbook) (2017) by McClure, N. | Packt\n\n## Courses\n\n* [Classification Models](https://www.udacity.com/course/classification-models--ud978) (2018) by alteryx and tab|eau | Udacity\n\n* [Computer Vision Crash Course](https://www.youtube.com/watch?v=-4E2-0sxVUM) (2018) | PBS Digital Studios\n\n* [Deep Learning](https://www.kaggle.com/learn/deep-learning) (2018) by kaggle\n\n* [Intro to Deep Learning](https://www.udacity.com/course/deep-learning--ud730) (2018) by Google | Udacity\n\n* [Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120) (2018) by kaggle | Udacity\n\n* [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\n\n* [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.com/machine-learning/crash-course/) (2018) by Google\n\n* [ML Practicum: Image Classification](https://developers.google.com/machine-learning/practica/image-classification/) (2018) by Google\n\n* [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\n\n## Companies\n* [SpaceKnow](https://www.spaceknow.com/)\n\n\n## Credits\nInspired by [awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FdeepVector%2Fgeospatial-machine-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FdeepVector%2Fgeospatial-machine-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FdeepVector%2Fgeospatial-machine-learning/lists"}