The challenges of integrating explainable artificial intelligence into GeoAI
Abstract Although explainable artificial intelligence (XAI) promises considerable progress in glassboxing deep learning models, there are challenges in applying XAI to geospatial artificial intelligence (GeoAI), specifically geospatial deep neural networks (DNNs).
Jin Xing, Renee Sieber
wiley +1 more source
Towards Understanding the Geospatial Skills of ChatGPT: Taking a Geographic Information Systems (GIS) Exam [PDF]
This paper examines the performance of ChatGPT, a large language model (LLM), in a geographic information systems (GIS) exam. As LLMs like ChatGPT become increasingly prevalent in various domains, including education, it is important to understand their ...
Cui, Wencong +3 more
core +2 more sources
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)
Sandy Liwan, Philia Christi Latue
openaire +1 more source
Artificial intelligence and visual analytics in geographical space and cyberspace: Research opportunities and challenges [PDF]
In recent decades, we have witnessed great advances on the Internet of Things, mobile devices, sensor-based systems, and resulting big data infrastructures, which have gradually, yet fundamentally influenced the way people interact with and in the ...
Bandrova, T +28 more
core
A Spatiotemporal Intelligent Framework and Experimental Platform for Urban Digital Twins
This work emphasizes the current research status of the urban Digital Twins to establish an intelligent spatiotemporal framework. A Geospatial Artificial Intelligent (GeoAI) system is developed based on the Geographic Information System and Artificial ...
Jinxing Hu +7 more
doaj +1 more source
Know, Know Where, Know Where Graph:A densely connected, cross-domain knowledge graph and geo-enrichment service stack for applications in environmental intelligence [PDF]
Knowledge graphs (KGs) are a novel paradigm for the representation, retrieval, and integration of data from highly heterogeneous sources. Within just a few years, KGs and their supporting technologies have become a core component of modern search engines,
Cai, Ling +25 more
core +3 more sources
SPATIAL MACHINE LEARNING FOR MONITORING TEA LEAVES AND CROP YIELD ESTIMATION USING SENTINEL-2 IMAGERY, (A Case of Gunung Mas Plantation, Bogor) [PDF]
Indonesia's tea production and export volume have fluctuated with a downward trend in the last five years, partly due to the increasingly competitive world tea quality.
Manessa, Masita Dwi Mandini +1 more
core +2 more sources
In this article the challenge of detecting areas linked to transnational environmental crimes in the Amazon rainforest is addressed using Geospatial Intelligence data, open access Sentinel-2 imagery provided by the Copernicus programme, as well as the ...
Jairo J. Pinto-Hidalgo +1 more
doaj +1 more source
From Geospatial Data Cube to AI Cube: the Open Geospatial Engine (OGE) Approach [PDF]
The Earth Observation (EO) analytics are moving from local systems to online cloud computing platforms such as Google Earth Engine (GEE) and Open Geospatial Engine (OGE).
P. Yue +5 more
doaj +1 more source
GeoAI: Where machine learning and big data converge in GIScience [PDF]
In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIScience. As the new fuel of geospatial research, GeoAI leverages recent breakthroughs in machine learning and advanced computing to achieve scalable ...
Li, Wenwen
core +1 more source

