Artificial Intelligence for Multisource Geospatial Information

Bordogna, Gloria; Fugazza, Cristiano

doi:10.3390/ijgi12010010

Cited by 3 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Geospatial data includes information that information related to a particular location or area, such as maps, satellite data, drone data, sensor data, and others related to the geography and topography of an area [17]. Utilization of this AI technology can help process hydrological, topographical, and rainfall data to identify areas with high flood risk and plan effective mitigation strategies [18]. With AI technology, spatial data can be processed quickly and accurately to facilitate decision-making in flood mitigation planning in Bengkulu City.…”

Section: Introductionmentioning

confidence: 99%

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Apriana,

Al Ansory,

Agustina

et al. 2024

J. Technomaterial Phys.

View full text Add to dashboard Cite

Bengkulu City is an area prone to flooding due to its proximity to the river estuary. Flooding in Bengkulu City usually occurs during the rainy season, when high rainfall and overflowing rivers can cause inundation in several areas around the city. This research aims to identify flood-prone areas in Bengkulu City through a Geographic Artificial Intelligence (Geo-AI) approach. Geo-AI is an artificial intelligent machine with geospatial data, including satellite images and weather data, whose data analysis is more accurate and efficient in identifying flood-prone areas. Geographic Information System (GIS) can present objects of flood-prone areas from the real world in digital form. Through Google Earth Engine (GEE), satellite imagery data and other geospatial data are processed and analyzed using artificial intelligence algorithms to identify flood patterns and frequently flooded areas. The final result of this research is a map of flood-prone areas in Bengkulu City. On the map, it can be concluded that the red-colored areas including Rawa Makmur, Tanjung Agung, Bentiring, Kebun Tebeng, Penurunan, Sukarami, Pekan Sabtu, and Air Sebakul are areas that are often flooded so that these areas can be confirmed as flood-prone.

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Apriana,

Al Ansory,

Agustina

et al. 2024

J. Technomaterial Phys.

View full text Add to dashboard Cite

show abstract

“…One field that continues to experience rapid progress is geography and remote sensing. The use of Geographic Artificial Intelligence (GeoAI) technology and Unmanned Aerial Vehicles (UAV) has provided new opportunities to collect, analyze, and visualize geographic data efficiently and accurately (Bordogna & Fugazza, 2022). Panggang Island, located in DKI Jakarta Province, Indonesia, is one of the areas facing challenges in sustainable urban development.…”

Section: Introductionmentioning

confidence: 99%

Geographic Artificial Intelligence and Unmanned Aerial Vehicles Application for Correlation Analysis of Settlement Density and Land Surface Temperature in Panggang Island Jakarta

Pertuack,

Latue

2023

Buana j. Geografi Ekologi dan Kebencanaan

View full text Add to dashboard Cite

This research aims to understand the relationship between high settlement density and land surface temperature associated with the Urban Heat Island (UHI) phenomenon. The data processing method involves collecting settlement density data using UAVs equipped with thermal sensors, as well as using GeoAI, namely GEE, to analyze LST on Panggang Island. The results showed a positive relationship between settlement density and LST on Panggang Island, with high settlement density contributing to an increase in ground surface temperature. The benefits of the application of GeoAI and UAVs in this analysis include accurate mapping, understanding the impacts of urbanization, sustainable urban planning, and fact-based decision-making. It is hoped that this research can contribute to better urban management and reduction of environmental impacts in Pulau Panggang, DKI Jakarta.

show abstract

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

Razavi-Termeh,

Sadeghi-Niaraki,

Yao

et al. 2024

Journal of Environmental Management

View full text Add to dashboard Cite

Artificial Intelligence for Multisource Geospatial Information

Abstract: The term Geospatial Artificial Intelligence (GeoAI) is quite cumbersome, and it has no single, shared definition [...]

Cited by 3 publications

References 11 publications

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Mapping Flood-Prone Areas Using GIS Through as Geo-Artificial Intelligence (Geo-Ai) Approach in Bengkulu City

Geographic Artificial Intelligence and Unmanned Aerial Vehicles Application for Correlation Analysis of Settlement Density and Land Surface Temperature in Panggang Island Jakarta

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach

Contact Info

Product

Resources

About