Azure / pixel_level_land_classificationLinks
Tutorial demonstrating how to create a semantic segmentation (pixel-level classification) model to predict land cover from aerial imagery. This model can be used to identify newly developed or flooded land. Uses ground-truth labels and processed NAIP imagery provided by the Chesapeake Conservancy.
☆281Updated 6 years ago
Alternatives and similar repositories for pixel_level_land_classification
Users that are interested in pixel_level_land_classification are comparing it to the libraries listed below
Sorting:
- A sample project demonstrating how to extract building footprints from satellite images using a semantic segmentation model. Data from th…☆108Updated 5 years ago
- Analyzing Sentinel-2 satellite data in Python with Keras (repository of our talks at Minds Mastering Machines 2019 and PyCon 2018)☆194Updated 4 years ago
- Land Cover Mapping☆202Updated 2 years ago
- Deep learning applied to georeferenced datasets☆187Updated last week
- Deep learning courses and projects☆95Updated 7 years ago
- ArcGIS built-in python raster functions for deep learning to get you started fast.☆198Updated last year
- Data Preparation for Satellite Machine Learning☆473Updated 2 years ago
- Satellite image processing pipeline to classify land-cover and land-use☆88Updated 8 years ago
- Collection of Remote Sensing Resources☆111Updated 3 years ago
- Land Use Classification using Convolutional Neural Network in Keras☆51Updated 8 years ago
- Code for constructing the urban environments dataset and for land use classification via convolutional neural networks☆120Updated 7 years ago
- Dstl Satellite Imagery Feature Detection☆145Updated 8 years ago
- Experiments with satellite image data☆123Updated 6 years ago
- Tutorial of basic remote sensing and GIS methodologies using open source software (GDAL in Python or R)☆257Updated 6 years ago
- Change Detection with Google Earth Engine Imagery☆139Updated 6 years ago
- Deep learning with otb (mirror of https://forgemia.inra.fr/orfeo-toolbox/otbtf)☆166Updated 6 months ago
- Examples of using Raster Vision on open datasets☆174Updated 5 years ago
- Open source notebooks to create state-of-the-art detection, segmentation, & classification of buildings on drone/aerial imagery with deep…☆198Updated 5 years ago
- Code for training and testing deep learning based land cover models.☆95Updated 2 years ago
- 🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis☆422Updated 2 years ago
- Building detector algorithms from second SpaceNet Challenge☆194Updated 5 years ago
- Automated download of Sentinel-2 L1C data from ESA (through wget) //olivierhagolle.github.io/Sentinel-download☆193Updated 7 years ago
- 8th place solution to Zindi's FarmPin Crop Detection Challenge☆137Updated 5 years ago
- Mask R-CNN in GRASS GIS☆41Updated 4 years ago
- An open-source semi-automated processing chain for urban OBIA classification.☆75Updated 5 years ago
- Repository containing model development, training, and execution for the CAFO satellite imagery detection process.☆56Updated 7 years ago
- Tree detection from aerial imagery in Python☆273Updated last week
- Python scripts for the textbook "Image Analysis, Classification and Change Detection in Remote Sensing, Fourth Revised Edition"☆110Updated 2 years ago
- Use Mask R-CNN/Keras/TensorFlow and OSM to find features in satellite images for fun.☆504Updated 2 years ago
- Mask-RCNN baseline for the crowdAI Mapping Challenge☆262Updated 4 years ago