Learning to Segment Publicly Accessible Green Spaces with Visual and Semantic Data


Jian Gao (Queen's University Belfast), Niall McLaughlin (The Queen's University Belfast), Joanna Sara Valson (The Queen's University Belfast), Neil Anderson (The Queen's University Belfast), Ruth Hunter (The Queen's University Belfast)
The 35th British Machine Vision Conference
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Abstract

The study of the health effects of Publicly Accessible Green Spaces (PAGS), such as parks and urban greenways, has received increasing attention in environmental sciences and public health research. However, the lack of relevant data and methods for PAGS mapping limits this work. To our best knowledge, most of the existing studies of PAGS mapping are manual, limited to small regions, and do not generalise geographically. In this paper, we introduce a first-of-its-kind dataset - the Northern Ireland Publicly Accessible Green Spaces (PAGS-NI) dataset. Unlike existing datasets that typically consider only visual remote sensing data, our PAGS-NI dataset combines high-resolution, multi-band remote sensing data, geographical information data and activity data with hand-verified PAGS ground truth. Using this dataset, we develop a semantic segmentation model for automatic and scalable PAGS mapping that fuses these different data sources. Our model is able to predict PAGS on unseen places given appropriate training, which exceeds prior art. Furthermore, we show that our model trained solely on Northern Ireland can generalise to PAGS prediction for areas in the United States. Our model and dataset have the potential to advance large-scale PAGS studies in environmental science and public health research.

Citation

@inproceedings{Gao_2024_BMVC,
author    = {Jian Gao and Niall McLaughlin and Joanna Sara Valson and Neil Anderson and Ruth Hunter},
title     = {Learning to Segment Publicly Accessible Green Spaces with Visual and Semantic Data},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0043.pdf}
}


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