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.
☆278Updated 5 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…☆106Updated 5 years ago
- Experiments with satellite image data☆121Updated 5 years ago
- Land Cover Mapping☆204Updated 2 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
- Deep learning courses and projects☆95Updated 6 years ago
- Code for training and testing deep learning based land cover models.☆96Updated 2 years ago
- Data Preparation for Satellite Machine Learning☆467Updated last year
- ArcGIS built-in python raster functions for deep learning to get you started fast.☆198Updated 9 months ago
- Satellite image processing pipeline to classify land-cover and land-use☆85Updated 8 years ago
- Examples of using Raster Vision on open datasets☆174Updated 4 years ago
- Dstl Satellite Imagery Feature Detection☆144Updated 7 years ago
- Collection of Remote Sensing Resources☆108Updated 3 years ago
- Building detector algorithms from second SpaceNet Challenge☆194Updated 5 years ago
- A walkthrough of some Google Earth Engine Features, as well as using the data in TensorFlow☆80Updated 8 years ago
- Sample scripts and notebooks on processing satellite imagery Python Geospatial raster☆194Updated last month
- Project to train/test convolutional neural networks to extract buildings from SpaceNet satellite imageries.☆197Updated last year
- Deep learning applied to georeferenced datasets☆168Updated 3 weeks ago
- A series of Jupyter notebook to learn Google Earth Engine with Python☆285Updated last year
- Packages intended to assist in the preprocessing of SpaceNet satellite imagery data corpus to a format that is consumable by machine lear…☆249Updated 5 years ago
- Open source notebooks to create state-of-the-art detection, segmentation, & classification of buildings on drone/aerial imagery with deep…☆197Updated 5 years ago
- Geoseg - A Computer Vision Package for Automatic Building Segmentation and Outline extraction☆110Updated 4 years ago
- Automated Building Detection using Deep Learning☆102Updated 6 months ago
- Road network extraction from satellite imagery, with speed and travel time estimates☆191Updated 2 years ago
- Sentinel Hub Cloud Detector for Sentinel-2 images in Python☆458Updated 9 months ago
- 8th place solution to Zindi's FarmPin Crop Detection Challenge☆137Updated 5 years ago
- Code for constructing the urban environments dataset and for land use classification via convolutional neural networks☆116Updated 7 years ago
- An open-source semi-automated processing chain for urban OBIA classification.☆75Updated 4 years ago
- Winning building footprint detector implementations from the SpaceNet challenges.☆157Updated 8 years ago
- Satellite Image Classification using semantic segmentation methods in deep learning☆311Updated 2 years ago
- CosmiQ Works Geospatial Machine Learning Analysis Toolkit☆427Updated 2 years ago