Pinned Repositories
Broad-UNet
Deep-learning-for-multi-year-ENSO-Reproduction
Deep learning for multi-year ENSO forecasts Reproduction
EMIT-Data-Resources
This repository provides guides, short how-tos, and tutorials to help users access and work with data from the Earth Surface Mineral Dust Source Investigation (EMIT) mission.
heatnet
Heatwavetracker
LiDAR_PointCloud_ToolBox
This toolbox uses spatial grid index to organize LIDAR point cloud data, with model builder and Python script building space model, and finally implementing a progressive morphological filtering algorithm for discrete point cloud processing; This toolbox also contains a new topographical feature point extraction method with a combination of hydrology water analysis and TIN geometric analysis.
mountain-solar-radiation-map
MSR
NicheMapR
R implementation of Niche Mapper software for biophysical modelling
ProfoundData
Checking and benchmarking of dynamic vegetation models
PythonDataScienceHandbook
Jupyter Notebooks for the Python Data Science Handbook
geogismx's Repositories
geogismx/PythonDataScienceHandbook
Jupyter Notebooks for the Python Data Science Handbook
geogismx/deep-learning-with-python-notebooks
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
geogismx/drought_app_v01
Visualize the results of the historic 2018 drought in The Netherlands.
geogismx/ecodash
geogismx/erosivity
calculates rainfall erosivity using precipitation time series
geogismx/Fundamentals-of-Digital-Image-and-Video-Processing-course
This repository contains python notebooks taken from Coursera course "Fundamentals of Digital Image and Video Processing", taught at Northwestern University
geogismx/gdal-cheat-sheet
Cheat sheet for GDAL/OGR command-line tools
geogismx/geoJSONToShpFile
This code converts GeoJSON to shape files.
geogismx/ghcnd_access
The ghcnd_access toolbox is utilised to extract data from the Global Historical Climatology Network (GHCN)-Daily database and change the .dly file format into a more accessible structure. The toolbox can be run in either MATLAB or open source alternative GNU Octave.
geogismx/GSFLOW-GRASS
Generates inputs for and runs the coupled groundwater-surface water model "GSFLOW"
geogismx/hydro-engine
Hydro Engine is a service and a command-line tool to query static and dynamic hydrographic Earth Observation data
geogismx/interpolation
Spatial interpolation methods.
geogismx/Kalman-and-Bayesian-Filters-in-Python
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.
geogismx/label-maker
Data Preparation for Satellite Machine Learning
geogismx/Lottery
C#抽奖程序1-文字版 （动态配置抽奖人名单，名字上下双向循环滚动，可调滚动速度）
geogismx/MCD10A1
a robust MODIS snow cover and phenology product (Google Earth Engine codebase)
geogismx/modis-gapfilling
geogismx/ogr2ft
Uploads features from OGR source to Google Fusion Table
geogismx/PCR-GLOBWB_model
PCR-GLOBWB (PCRaster Global Water Balance) is a large-scale hydrological model intended for global to regional studies and developed at the Department of Physical Geography, Utrecht University (Netherlands). Contact: Edwin Sutanudjaja (E.H.Sutanudjaja@uu.nl).
geogismx/phenofit
A state-of-the-art Vegetation Phenology extraction package: phenofit
geogismx/pixel_level_land_classification
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.
geogismx/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
geogismx/RVIC
RVIC Streamflow Routing Model
geogismx/scipy-devsummit-2017-talk
Scientific Programming with the SciPy Stack
geogismx/SOC-Mapping-Cookbook
Soil Organic Carbon mapping Cookbook
geogismx/StatisticalDownscaling
Statistical downscaling
geogismx/tensorflow
Computation using data flow graphs for scalable machine learning
geogismx/thesis
geogismx/Trevisani-2015
MAD: robust image texture analysis for applications in high resolution geomorphometry, S. Trevisani and M. Rocca.
geogismx/WaterNet
A convolutional neural network that identifies water in satellite images.