qiushiengineer

qiushiengineer's Stars

• thunlp/GNNPapers

  Must-read papers on graph neural networks (GNN)

  15.9k601173k

• GeoPyTool/GeoPyTool

  An application based on Python and designed as a solution for geology related daily work.

  Language:Python247182260

• thilowrona/seismic_deep_learning

  A couple of python scripts to interpret geological structures from geophysical images using deep learning

  Language:Jupyter Notebook1979374

• eleanorlutz/moon_geology_atlas_of_space

  Code, data, and instructions for mapping the geology of the moon

  Language:Jupyter Notebook1045013

• ondrolexa/apsg

  Structural geology package for Python

  Language:Python10082228

• mhscience/landslides_detection

  Machine learning tool to detect landslides from optical satellite imagery

  Language:Python876630

• akrabat/avalanche

  A theme for landslide

  Language:CSS6851122

• NASA-DEVELOP/DRIP-SLIP

  DRIP and SLIP Landslide Detection Package

  Language:Python6518138

• zhoukaisheng/Landslide-Susceptibility

  Landslide susceptibility mapping by deep learning and slope unit

  Language:Python604216

• joferkington/geo_image_processing_tutorial

  Transform 2020 Geologic Image Processing Tutorial

  Language:Jupyter Notebook486021

• usgs/landslides-trigrs

  3791120

• MinZHANG-WHU/CDMI-Net

  The implementation of CDMI-Net in Paper - Deep Multiple Instance Learning for Landslide Mapping

  Language:Python25329

• david-wilson/colorado-geology-geodjango

  This is a simple GeoDjango app that uses Colorado geologic data to show the surface geologic unit for the user's location.

  Language:Python235011

• karstnet/karstnet

  Karstnet is a python3 project providing tools for the statistical analysis of karstic networks.

  Language:Jupyter Notebook16718

• maurorossi/LAND-SE

  LANDslide Susceptibility Evaluation

  Language:R145

• sandricionut/Landslide-Analysis

  Landslides analysis, ArcGIS, Python and R

  Language:Python135

• SatyanarayanaVelamala/Rock-Identification-Using-Deep-Convolution-Neural-Network

  Rocks are a fundamental component of Earth. The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. It is a basic part of geological surveying and research, and mineral resources exploration. The automatic identification of rock type in the field would aid geological surveying, education, and automatic mapping. Working conditions in the field generally limit identification to visual methods, including using a magnifying glass for fine-grained rocks. Visual inspection assesses properties such as colour, composition, grain size, and structure. The attributes of rocks reflect their mineral and chemical composition, formation environment, and genesis. The colour of rock reflects its chemical composition. But these analysis is time taken process to identify the rocks.Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. Solution: Deep learning is receiving significant research attention for pattern recognition and machine learning. Its application here has effectively identified rock types from images captured in the field. This paper proposes an accurate approach for identifying rock types in the field based on image analysis using deep convolutional neural networks. The results show that the proposed approach based on deep learning represents an improvement in intelligent rock-type identification and solves several difficulties facing the automated identification of rock types in the field.Who are experienced in the field of geological they can identify the rocks easily. But who are new to the field, it can help to identify the type of rock.

  Language:Jupyter Notebook124

• usgs/landslides-thresholds

  Thresh – Software for Tracking Rainfall Thresholds for Landslide and Debris Flow Occurrence (A collection of Fortran programs, Python scripts, shell scripts and Windows batch files along with sample data)

  Language:Fortran11505

• mukhlis-mah/karstolution

  The First Speleothem δ18O Model Integrating Karst Hydrological and In-Cave Fractionation Processes

  Language:Python54

• shikarukitake/Lithofacies-classification

  AI Challenge: Lithofacies classification based on rock image recognition using deep learning

  Language:Jupyter Notebook33

• legiongis/karstography

  karst feature mapping for crawford stewardship project

  Language:Python22220

• fabrizioperuzzo/stoch_karst

  Stochastic analysis of karst distribution

  10

• MiKom/karstgen

  Karst cave generator. This project is accompanying my masters thesis in thesis repo.

  Language:C++1200

• NenadPantelic/Rock-paper-scissors-image-gesture-recognition

  Rock-paper-scissors gesture recognition on images

  Language:Python10