GRS's repositories
awesome-datascience
:memo: An awesome Data Science repository to learn and apply for real world problems.
awesome-satellite-imagery-datasets
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
38-Cloud-A-Cloud-Segmentation-Dataset
This data set includes Landsat 8 images and their manually extracted pixel-level ground truths for cloud detection.
95-Cloud-An-Extension-to-38-Cloud-Dataset
A huge dataset for binary segmentation of clouds in satellite images
awesome-multimodal-remote-sensing-classification
List of datasets, papers, and codes related to multimodal/multisource/multisensor remote sensing classification
big_data_and_urban_computing_course
Course materials for big data and urban computing (2020 fall semester).
Cloud-Net-A-semantic-segmentation-CNN-for-cloud-detection
A semantic segmentation CNN for cloud detection
cuFSDAF
cuFSDAF is an enhanced FSDAF algorithm parallelized using GPUs. In cuFSDAF, the TPS interpolator is replaced by a modified Inverse Distance Weighted (IDW) interpolator. Besides, computationally intensive procedures are parallelized using the Compute Unified Device Architecture (CUDA), a parallel computing framework for GPUs. Moreover, an adaptive domain-decomposition method is developed to adjust the size of sub-domains according to hardware properties adaptively and ensure the accuracy at the edges of sub-domains.
custom-scripts
A repository of custom scripts to be used with Sentinel Hub
cuSTSG
cuSTSG is a GPU-enabled spatial-temporal Savitzky-Golay (STSG) program based on the Compute Unified Device Architecture (CUDA). Firstly, the cosine similarity between time-series data of adjacent years is used to identify similar years without land cover type changes, hence to exclude the years with inaccurate quality flags from generating NDVI seasonal growth trajectory. Secondly, the computational performance is improved by reducing redundant computations, and parallelizing the computationally intensive procedures using the CUDA for GPUs.
earthengine-py-notebooks
A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping
elevation-gp-python
ArcGIS elevation analysis tool that allows you to set up an in-house viewshed geoprocessing service.
eo-flow
Collection of TensorFlow 2.0 code for Earth Observation applications
GeoCA
Geographical Simulation Application via Cellular Automata (GeoCA)
mask-to-polygons
Routines for extracting and working with polygons from semantic segmentation masks
Mixed_Cell_Cellullar_Automata
The Mixed-Cell Cellullar Automata (MCCA) provides a new approach to enable more dynamic mixed landuse modeling to move away from the analysis of static patterns. One of the biggest advantages of mixed-cell CA models is the capability of simulating the quantitative and continuous changes of multiple landuse components inside cells.
Open-Space-Cellular_Automata
A spatio-temporal approach based on Cellular Automata (CA) for simulating the spatial dynamics of open spaces (include urban green spaces, parks, squares, trails, courtyards, and other natural spaces), by considering a set of spatial data that represents the infrastructural and socio-economic factors, namely the OS-CA (Open Space Cellular Automaton) model. The dynamic sub-model for OS is used to generate scenarios with different parameters (e.g. mean construction delays and mean area) for exploring the effects of planning policies on the future distribution of open space. The OS-CA considers the interactions and inter-attraction between open space and urban land in the simulation process. The proposed model can accurately predict the emergence of some open spaces.
Patch-generating_Land_Use_Simulation_Model
The PLUS model integrates a rule mining framework based on Land Expansion Analysis Strategy (LEAS) and a CA model based on multi-type Random Patch Seeds (CARS), which was used to understand the drivers of land expansion and project landscape dynamics.
Py4Geo
Satellite Image Analytics and Earth Data Science Experiments in Python
raster-deep-learning
ArcGIS built-in python raster functions for deep learning to get you started fast.
raster-functions
A curated set of lightweight but powerful tools for on-the-fly image processing and raster analysis in ArcGIS.
s2p
Satellite Stereo Pipeline
s2p-1
This repository is not maintained, please use https://github.com/centreborelli/s2p instead.
SuperCugersMappingSystem
Surveying and Mapping System for error adjustment of SUPERCUGERS team
TorchCRF
An Inplementation of CRF (Conditional Random Fields) in PyTorch 1.0