Chandi Witharana

Orcid: 0000-0002-7587-535X

According to our database1, Chandi Witharana authored at least 15 papers between 2016 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping.
Remote. Sens., March, 2024

Hyperparameter Optimization for Large-Scale Remote Sensing Image Analysis Tasks: A Case Study Based on Permafrost Landform Detection Using Deep Learning.
IEEE Access, 2024

2023
Can Plot-Level Photographs Accurately Estimate Tundra Vegetation Cover in Northern Alaska?
Remote. Sens., April, 2023

Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features.
CoRR, 2023

2022
Automated Detection of Retrogressive Thaw Slumps in the High Arctic Using High-Resolution Satellite Imagery.
Remote. Sens., 2022

Convolutional Neural Networks for Automated Built Infrastructure Detection in the Arctic Using Sub-Meter Spatial Resolution Satellite Imagery.
Remote. Sens., 2022

Real-time GeoAI for high-resolution mapping and segmentation of arctic permafrost features: the case of ice-wedge polygons.
Proceedings of the 5th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery, 2022

2021
An Object-Based Approach for Mapping Tundra Ice-Wedge Polygon Troughs from Very High Spatial Resolution Optical Satellite Imagery.
Remote. Sens., 2021

Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data.
Remote. Sens., 2021

Multi-Dimensional Remote Sensing Analysis Documents Beaver-Induced Permafrost Degradation, Seward Peninsula, Alaska.
Remote. Sens., 2021

2020
Understanding the Effects of Optimal Combination of Spectral Bands on Deep Learning Model Predictions: A Case Study Based on Permafrost Tundra Landform Mapping Using High Resolution Multispectral Satellite Imagery.
J. Imaging, 2020

Use of Very High Spatial Resolution Commercial Satellite Imagery and Deep Learning to Automatically Map Ice-Wedge Polygons across Tundra Vegetation Types.
J. Imaging, 2020

2018
Using Deep Learning to Identify Utility Poles with Crossarms and Estimate Their Locations from Google Street View Images.
Sensors, 2018

Deep Convolutional Neural Networks for Automated Characterization of Arctic Ice-Wedge Polygons in Very High Spatial Resolution Aerial Imagery.
Remote. Sens., 2018

2016
An Object-Based Image Analysis Approach for Detecting Penguin Guano in very High Spatial Resolution Satellite Images.
Remote. Sens., 2016


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