kushanavbhuyan Goto Github PK
Name: Kushanav Bhuyan
Type: User
Company: University of Padova
Bio: PhD Researcher with interest in landslide hazard and risk, numerical modelling, and deep learning models.
Location: Italy
Name: Kushanav Bhuyan
Type: User
Company: University of Padova
Bio: PhD Researcher with interest in landslide hazard and risk, numerical modelling, and deep learning models.
Location: Italy
This repository is for the Python Elective course at the Faculty of ITC, University of Twente, Netherlands. The repository consists of the data, model and instructions required to perform building footprint extraction from satellite imagery using a U-Net model.
Information about buildings is, sufficed to say, a very important aspect not just for urban land registry or transportation but also for disaster/hazard risk assessment. Specifically, typological attributes of buildings like number of residents living in them, number of floors, and many more. The study aims at figuring and capturing the typological attributes of the buildings by incorporating deep learning and other proxy information as a means of detecting and characterising the buildings.
This repository contains code for our upcoming work on landslide kinematic separation, meaning, the demarcation between the source and runout zones.
The application of Google Earth Engine is used for analysis of flood extent and damage assessment of western Ugandan town of Kasese.
This is a GEE code for forest monitoring using Sentinel-1 to assess vegetation loss for the western Ugandan cityof Kasese.
A global homogenised database of active faults maintained by the GEM Foundation
A python library to estimate likely triggers of mapped landslides
An FCN based model for detecting landslide footprints based on existing landslide inventories of Kerala, 2018.
Contains code for measuring landslide ellipticity and length-to-width ratio
By using the pre-trained models, this method enables quick and simple mapping of landslides at various spatiotemporal scales. The method also offers the adaptability of re-training a pretrained model to identify landslides caused by both rainfall and earthquakes on different target locations.
Introducing a transfer learning approach to map landslides temporally over a given spatial location.
A physically-based multi-hazard modelling approach with OpenLisem and PC Raster.
This repo reproduces the results of TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Tensorflow Implementation of TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
This repository contains sample codes for the automatic detection of landslide movement types based on topological information, using a landslide's 3D shape.
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JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
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