GRS's repositories
wolfram-cellular-automata-python
Wolfram's cellular automata in python.
shapes
Class hierarchy for representing 2D/3D shapes
Cellular-Automata-Traffic-Simulation
Research on The Impact of Road Traffic Around on Opening Residential Community Based on Cellular Automaton
shapeViewer
GUI for manipulation and edition of geometric shapes and scenes
plant_stress_phenotyping
Software to accompany plant stress phenotyping dataset and analysis papers
ASK
JEI 2019: Adaptive scalable kernel for hyperspectral image classification
convoca
Predict and analyze cellular automata using convolutional neural networks
whitebox-tools
An advanced geospatial data analysis platform
Progressive-Morphological-Filter-for-FME
A progressive morphological filter for DEM generation usable in FME
Wolfram_Simulations
Python project that creates cellular automata simulations and displays them in various ways.
geoapi
GeoAPI provides a set of interfaces in programming languages (currently Java and Python) for geospatial applications. The GeoAPI interfaces closely follow OGC specifications, adaptated to match the expectations of programmers.
CellularAutomatas
Patterns generated by different automata rules.
Spectral-Angular-Classification-of-Satellite-Image
A software package is built for display and classification of Hyperspectral Images captured byIMS-1 HySI sensor has been developed using SAM.The construction and display of the 3-D cube by considering all the 64 bands of image at a time. The identification of classes in the Hyperspectral Image using a supervised classification algorithm called the Spectral Angle Mapper Algorithm. Results are obtained to read and reorganize multiple 2-D datasets into a single compact 3D dataset cube.Thematic Information Extraction — Supervised Classification Remotely sensed data may be analyzed to extract use- ful thematic information. This transforms the data into in- formation. For example, themes may include land-cover, water bodies, and clouds. The classification may be per- formed using supervised, unsupervised and fuzzy set clas- sification approaches. In a supervised image classification, the identity and lo- cation of some of the land-cover types should be known beforehand through a combination of fieldwork, analy- sis of aerial photography, maps, and personal experience. The analyst attempts to locate sites in the remotely sensed data that represent homogeneous examples of these known land-cover types. These areas are commonly referred to as training sites because the spectral characteristics of these known areas are used to train the classification algorithm for eventual land-cover mapping of the remainder of the image. Multivariate statistical parameters such as means, standard deviations, and covariance matrices are calculated for each training site. Every pixel both inside and outside these training sites is then evaluated and assigned to the class where it has the highest likelihood of being a mem- ber. This is often referred to as hard classification because a pixel is assigned to only one class (e.g., forest), even though the sensor records the radiant flux from a mixture of biophysical materials, for example: 10% bare soil, 20% scrub shrub, 70% forest.
EarthEngine_scripts
scripts and snippets for Google Earth Engine
cellularAutomata
a collection of cellular automata written in Haskell with Diagrams
GeneticAlgorithmsRepo
Genetic algorithm packages for Python.
Cellular-Automata-Based-Model-of-Urban-Spatial-Growth
Modelling and Simulation Course - Developed a cellular-automata based model for simulating future urban growth of a region (Ahmedabad District) and emphasized the conditions under which spontaneous growth, such as that which characterized the regeneration of inner cities and the location of edge cities, could be modelled. My role was to write the code in Java and output a figure depicting future urban growth prediction of a city/region.
Fire-Spread-Model
A fire spread stochastic approach to modeling wildfires using cellular automata concepts
gplearn
Genetic Programming in Python, with a scikit-learn inspired API
SAVI-tool
This repository contains all the code and documentation for the Simulation Accuracy & Validation Informatics (SAVI) tool: a convenient, freely-available GUI-based tool designed to facilitate and automate the validation of simulated land change models.
gaft
A Genetic Algorithm Framework in Python
GeneticAlgorithmsWithPython
source code from the book Genetic Algorithms with Python by Clinton Sheppard
gscpy
:satellite: Sentinel-1 Pre-Processing in GRASS GIS
stac-implementation
Tools for Implementation of STAC SPEC for SpaceNet dataset https://github.com/radiantearth/stac-spec/tree/dev
stac-spec
SpatioTemporal Asset Catalog specification - making geospatial assets openly searchable and crawlable
spatialist
A Python module for spatial data handling
py-dag
Python implementation of directed acyclic graph
stac-browser
A Vue-based STAC browser intended for static + dynamic deployment
pyrism
:satellite: Python bindings for Remote Sensing Models