Cristhian's starred repositories
crop-type-detection-ICLR-2020
Winning Solutions from Crop Type Detection Competition at CV4A workshop, ICLR 2020
techniques
Techniques for deep learning with satellite & aerial imagery
awesome-earthobservation-code
A curated list of awesome tools, tutorials, code, projects, links, stuff about Earth Observation, Geospatial Satellite Imagery
ML-Notebooks
:fire: Machine Learning Notebooks
public-apis
A collective list of free APIs
awesome-satellite-imagery-datasets
List of satellite imagery datasets with annotations for computer vision and deep learning
awesome-earthobservation-code
curated list of awesome tools, tutorials, code, helpful projects, links, stuff about Earth Observation and Geospatial stuff!
awesome-remote-sensing
Collection of Remote Sensing Resources
awesome-remote-sensing-change-detection
List of datasets, codes, and contests related to remote sensing change detection
stac-overflow
Winners of the STAC Overflow: Map Floodwater from Radar Imagery competition
PythonNumericalDemos
Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.
isce2-docs
Documents and tutorials for isce2
Crop-Classification
crop classification using deep learning on satellite images
Awesome-Earth-Artificial-Intelligence
A curated list of Earth Science's Artificial Intelligence (AI) tutorials, notebooks, software, datasets, courses, books, video lectures and papers. Contributions most welcome.
ee-rgb-timeseries
Earth Engine JS module to color time series chart points as stretched 3-band RGB.
Awesome-GEE
A curated list of Google Earth Engine resources
earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping
GeoPredictor
This microservice will be the one in charge of retriving the ML predictions from the trained models
awesome-gee-community-datasets
Community Datasets added by users and made available for use at large
InstanceSegmentation_Sentinel2
🌱 Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis