cloud-cover-winners
Cloud detection model
An online competition where machine learning models were trained to detect clouds in satellite images.
Code from the winning submissions for the On Cloud N: Cloud Cover Detection Challenge
25 stars
6 watching
4 forks
Language: Jupyter Notebook
last commit: over 2 years ago
Linked from 1 awesome list
Related projects:
| Repository | Description | Stars |
|---|---|---|
| | Automated cloud detection in Sentinel-2 imagery using machine learning | 437 |
| | Automated cloud and shadow detection for Landsat images using DEMs | 12 |
| | Tools for collecting and analyzing evidence from cloud platforms during incident response. | 467 |
| | Reconnaissance tool that gathers information about a Cloudflare-protected target to discover its server location using misconfigured DNS and old database records. | 2,251 |
| | Automated object detection from aerial imagery using OpenStreetMap data and deep learning. | 192 |
| | Automated cloud, shadow, and water masking software for satellite images | 172 |
| | A framework for training and deploying hyperspectral machine learning models for methane plume detection from satellite imagery | 44 |
| | Detects industrial smoke plumes from satellite images using machine learning models and remote sensing data. | 40 |
| | Provides tools and data structures to accelerate Lagrangian analysis in atmospheric, oceanic, and climate sciences. | 38 |
| | Automatically inferring 2D and 3D change detection maps from bitemporal optical images without relying on DSMs. | 29 |
| | A platform for comparing and evaluating AI and machine learning algorithms at scale | 1,779 |
| | A deep learning model that uses 3D point cloud data to improve the precision of single-stage object detection in autonomous vehicles. | 492 |
| | Developing deep learning models for Earth Observation using remote sensing images | 477 |
| | A deep learning framework for fast and accurate 3D object detection from LiDAR point clouds | 1,044 |
| | Automating road detection from aerial imagery using deep learning techniques. | 541 |