Segment anything, from space?
Author:
Affiliation:
1. Duke University,Electrical and Computer Engineering
2. Duke Kunshan University,Division of Natural and Applied Sciences
3. University of Montana,Computer Science
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10483279/10483282/10483606.pdf?arnumber=10483606
Reference61 articles.
1. Deep Learning Approaches Applied to Remote Sensing Datasets for Road Extraction: A State-Of-The-Art Review
2. Comparative Analysis of Segment Anything Model and U-Net for Breast Tumor Detection in Ultrasound and Mammography Images;Ahmadi,2023
3. Farm Parcel Delineation Using Spatio-temporal Convolutional Networks
4. Low-Power Neural Networks for Semantic Segmentation of Satellite Images
5. Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community
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1. An empirical study on the robustness of the segment anything model (SAM);Pattern Recognition;2024-11
2. RS-A2M: Zero-shot Prompt with Attention-based Unsupervised Segmentation for Remote Sensing;2024 IEEE Space, Aerospace and Defence Conference (SPACE);2024-07-22
3. Application and Evaluation of the AI-Powered Segment Anything Model (SAM) in Seafloor Mapping: A Case Study from Puck Lagoon, Poland;Remote Sensing;2024-07-18
4. Unsupervised Structural Damage Assessment from Space Using the Segment Anything Model (USDA-SAM): A Case Study of the 2023 Turkiye Earthquake;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
5. FMARS: Annotating Remote Sensing Images for Disaster Management Using Foundation Models;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07
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