Geospatial Artificial Intelligence (GeoAI)

Gao, Song

doi:10.1093/obo/9780199874002-0228

Cited by 20 publications

(6 citation statements)

References 58 publications

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“…In the context of refugee camps, AI models can be trained to recognize changes in camp layouts, infrastructure developments, and population distribution over time. This capability aids in comprehending the evolving urban morphology and predicting future growth trajectories [47].…”

Section: Artificial Intelligence (Ai) In Refugee Campsmentioning

confidence: 99%

Transitional Typologies of Refugee Camps in Jordan

Marji,

Chen,

Ravi Kumar

et al. 2024

Refugees and Migrants - Current Conditions and Future Trends [Working Title]

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This chapter critically examines the dynamic nature of refugee camps within Jordan’s unique socio-political context. Focusing on architectural, social, and environmental dimensions, the chapter unveils transitional typologies that challenge conventional ideas of permanence. Architecturally, it explores adaptive design solutions, emphasizing flexibility and sustainability. Socially, it delves into community-led initiatives and participatory planning, highlighting the agency and resilience of displaced populations. Environmentally, the chapter investigates the ecological impact of refugee settlements and advocates for sustainable practices. Through case studies and interdisciplinary analysis, this chapter offers insights into the transitional processes of refugee camps from temporary shelters to long-term dwellings, aiming to inform policymakers, practitioners, and researchers on enhancing the dignity, agency, and sustainability of displaced communities in Jordan and globally.

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Section: Artificial Intelligence (Ai) In Refugee Campsmentioning

confidence: 99%

Transitional Typologies of Refugee Camps in Jordan

Marji,

Chen,

Ravi Kumar

et al. 2024

Refugees and Migrants - Current Conditions and Future Trends [Working Title]

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“…Geospatial Artificial Intelligence (GeoAI) adalah pendekatan yang menggabungkan kecerdasan buatan (Artificial Intelligence/AI) dengan teknologi geospasial (Gao 2021). GeoAI mengintegrasikan data geografis dan geospasial dengan metode AI untuk memahami, menganalisis, dan membuat keputusan berdasarkan informasi yang terkait dengan lokasi atau spasial (Döllner 2020).…”

Section: Geospatial Artificial Intelligenceunclassified

Analisis Spasial Perubahan Suhu Permukaan Daratan Kota Kupang Menggunakan Pendekatan Geospatial Artificial Intelligence (GeoAI)

Liwan,

Latue

2023

Buana j. Geografi Ekologi dan Kebencanaan

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Suhu permukaan daratan di Kota Kupang mengalami peningkatan dari tahun 2018-2023, salah satu faktor penyebabnya yaitu terjadinya perkembangan lahan terbangun yang semakin meningkat setiap tahunnya. Penelitian ini menggunakan data citra Landsat 8 Collection 1 Tier 2 TOA Reflectance pada google earth engine. Untuk menganalisis suhu permukaan daratan (LST) pada citra Landsat 8 menggunakan pendekatan geospatial artificial intelligence (GeoAI) menggunakan pltafrom Google Earth Engine (GEE) berbasis cloud computing dengan menggunakan formula "Single Channel Algorithm" atau "Split-Window Algorithm". Hasil penelitian menunjukan bahwa nilai suhu permukaan daratan tertinggi di tahun 2018 berkisar 21,09ᵒ C – 30,79ᵒ C dan mengalami peningkatan di tahun 2023 menjadi 22,06ᵒ C – 34,99ᵒ C. Suhu permukaan pada kelas tinggi dan sangat tinggi terdistribusi di daerah pesisir yang megalami perkembangan lahan terbangun yang tinggi dan yang juga merupakan daerah pusat Kota Kupang. Hasil peneltian diharapkan dapat memberikan manfaat yang besar bagi Pemerintah setempat dalam merencanakan dan mengambil keputusan dalam berbagai sector diantaranya pengembangan sektor pertanian, pengelolaan sumber daya air, dan penanggulangan bencana.

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“…GCNs have been used for a wide range of applications in urban analytics, from socio-economic studies to urban sustainability (Zhang et al, 2022; Liu and Biljecki, 2022; Zhu et al, 2022; Gao, 2021). This article is not intended to provide a detailed literature review of GCN-based applications in urban studies.…”

Section: Introductionmentioning

confidence: 99%

Explainable spatially explicit geospatial artificial intelligence in urban analytics

Liu,

Zhang,

Biljecki

2023

Environment and Planning B: Urban Analytics and City Science

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Geospatial artificial intelligence (GeoAI) is proliferating in urban analytics, where graph neural networks (GNNs) have become one of the most popular methods in recent years. However, along with the success of GNNs, the black box nature of AI models has led to various concerns (e.g. algorithmic bias and model misuse) regarding their adoption in urban analytics, particularly when studying socio-economics where high transparency is a crucial component of social justice. Therefore, the desire for increased model explainability and interpretability has attracted increasing research interest. This article proposes an explainable spatially explicit GeoAI-based analytical method that combines a graph convolutional network (GCN) and a graph-based explainable AI (XAI) method, called GNNExplainer. Here, we showcase the ability of our proposed method in two studies within urban analytics: traffic volume prediction and population estimation in the tasks of a node classification and a graph classification, respectively. For these tasks, we used Street View Imagery (SVI), a trending data source in urban analytics. We extracted semantic information from the images and assigned them as features of urban roads. The GCN first provided reasonable predictions related to these tasks by encoding roads as nodes and their connectivities and networks as graphs. The GNNExplainer then offered insights into how certain predictions are made. Through such a process, practical insights and conclusions can be derived from the urban phenomena studied here. In this paper we also set out a path for developing XAI in future urban studies.

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Geospatial Artificial Intelligence (GeoAI)

Cited by 20 publications

References 58 publications

Transitional Typologies of Refugee Camps in Jordan

Transitional Typologies of Refugee Camps in Jordan

Analisis Spasial Perubahan Suhu Permukaan Daratan Kota Kupang Menggunakan Pendekatan Geospatial Artificial Intelligence (GeoAI)

Explainable spatially explicit geospatial artificial intelligence in urban analytics

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