Vinjou/Multimodal_urban_livability_evaluation_dataset

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@article{ZHOU2026115232,
title = {A transformer based multi-task deep learning model for urban livability evaluation by fusing remote sensing and textual geospatial data},
journal = {Remote Sensing of Environment},
volume = {334},
pages = {115232},
year = {2026},
issn = {0034-4257},
doi = {https://doi.org/10.1016/j.rse.2026.115232},
url = {https://www.sciencedirect.com/science/article/pii/S0034425726000027},
author = {Wen Zhou and Claudio Persello and Dongping Ming and Shaowen Wang and Alfred Stein},
keywords = {Urban livability, Multimodal deep learning, Satellite images, Digital surface model, Nighttime light remote sensing, Textual information},
abstract = {Livable cities enhance urban economic development, improve physical and mental health, foster well-being, and foster urban sustainability. Evaluating urban livability is therefore important for policymakers to develop urban planning and development strategies aimed at improving livability. Mainstream methods of evaluating urban livability assign different weights to diverse indicators extracted from survey data, statistical data, and geospatial data. To relieve such time-consuming and labor-intensive data collection, this study proposes a transformer-based multi-task multimodal regression (TMTMR) model for the simultaneous evaluation of urban livability focusing on five domain-specific scores. Pretrained state-of-the-art computer vision and natural language processing models serve as backbones to extract features from h

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dataset_licence({ "id": "Vinjou/Multimodal_urban_livability_evaluation_dataset" })
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stated_licence: "cc-by-nc-4.0"
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