Pre-trained regional models for extracting buildings from high resolution satellite imagery to support public health initiatives-Reference-Cited by-同舟云学术

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Pre-trained regional models for extracting buildings from high resolution satellite imagery to support public health initiatives

Published:2024-11 Issue: Volume:36 Page:101270
ISSN:2352-9385
Container-title:Remote Sensing Applications: Society and Environment
language:en
Short-container-title:Remote Sensing Applications: Society and Environment

Author:

Alexander Cici^ORCID,McKay Adam,Bhatt Kshitij,Raposo da Costa Lourenço Ana Lucia,Kaplan Brian,Santhana Gopala Krishnan Ravi Shankar

Funder

Bill and Melinda Gates Foundation

Publisher

Elsevier BV

Reference36 articles.

1. A survey on data‐efficient algorithms in big data era;Adadi;J Big Data,2021

2. Eff-UNet: a novel architecture for semantic segmentation in unstructured environment;Baheti,2020

3. High-resolution population estimation using household survey data and building footprints;Boo;Nat. Commun.,2022

4. Extracting rectified building footprints from traditional orthophotos: a new workflow;Chen;Sensors,2021

5. Large-scale individual building extraction from open-source satellite imagery via super-resolution-based instance segmentation approach;Chen;ISPRS J. Photogrammetry Remote Sens.,2023

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