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Transparent and Scalable Knowledge-B...

Song, Hunsoo,

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Transparent and Scalable Knowledge-Based Geospatial Mapping Systems for Trustworthy Urban Studies /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Transparent and Scalable Knowledge-Based Geospatial Mapping Systems for Trustworthy Urban Studies // Hunsoo Song.
作者:	Song, Hunsoo,
面頁冊數:	1 electronic resource (271 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-05, Section: A.
Contained By:	Dissertations Abstracts International86-05A.
標題:	Remote sensing. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31732964
ISBN:	9798346582120

Transparent and Scalable Knowledge-Based Geospatial Mapping Systems for Trustworthy Urban Studies /
Song, Hunsoo,

Transparent and Scalable Knowledge-Based Geospatial Mapping Systems for Trustworthy Urban Studies /Hunsoo Song. - 1 electronic resource (271 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: A.

This dissertation explores the integration of remote sensing and artificial intelligence (AI) in geospatial mapping, specifically through the development of knowledge-based mapping systems. Remote sensing has revolutionized Earth observation by providing data that far surpasses traditional in-situ measurements. Over the last decade, significant advancements in inferential capabilities have been achieved through the fusion of geospatial sciences and AI (GeoAI), particularly with the application of deep learning. Despite its benefits, the reliance on data-driven AI has introduced challenges, including unpredictable errors and biases due to imperfect labeling and the opaque nature of the processes involved. The research highlights the limitations of solely using data-driven AI methods for geospatial mapping, which tend to produce spatially heterogeneous errors and lack transparency, thus compromising the trustworthiness of the outputs. In response, it proposes novel knowledge-based mapping systems that prioritize transparency and scalability. This research has developed comprehensive techniques to extract key Earth and urban features and has introduced a 3D urban land cover mapping system, including a 3D Landscape Clustering framework aimed at enhancing urban climate studies. The developed systems utilize universally applicable physical knowledge of targets, captured through remote sensing, to enhance mapping accuracy and reliability without the typical drawbacks of data-driven approaches. The dissertation emphasizes the importance of moving beyond mere accuracy to consider the broader implications of error patterns in geospatial mappings. It demonstrates the value of integrating generalizable target knowledge, explicitly represented in remote sensing data, into geospatial mapping to address the trustworthiness challenges in AI mapping systems. By developing mapping systems that are open, transparent, and scalable, this work aims to mitigate the effects of spatially heterogeneous errors, thereby improving the trustworthiness of geospatial mapping and analysis across various fields. Additionally, the dissertation introduces methodologies to support urban pathway accessibility and flood management studies through dependable geospatial systems. These efforts aim to establish a robust foundation for informed urban planning, efficient resource allocation, and enriched environmental insights, contributing to the development of more sustainable, resilient, and smart cities.

English

ISBN: 9798346582120Subjects--Topical Terms:

535394
Remote sensing.

Transparent and Scalable Knowledge-Based Geospatial Mapping Systems for Trustworthy Urban Studies /
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