28 Aug 2023 Geospatial Artificial Intelligence (GeoAI)
This article covers “Daily Current Affairs” and the topic details “Geospatial Artificial Intelligence (GeoAI)”. The topic “Geospatial Artificial Intelligence (GeoAI)” has relevance in the “Science and Technology” section of the UPSC CSE exam.
For Prelims:
What is Geospatial Artificial Intelligence (GeoAI)?
For Mains:
GS3: Science and Technology
Why in the news?
Recently, the National Institute of Advanced Studies (NIAS) initiated a pilot project integrating GEOAI and random forest technology to monitor air quality in Bengaluru.
Geospatial Artificial Intelligence (GeoAI)
- Geospatial artificial intelligence (GeoAI) combines artificial intelligence (AI) with geospatial data, science, and technology for enhanced decision-making across various sectors such as to quickly grasp business potential, environmental effects, and operational risks.
- This fusion enables the extraction of valuable insights from geospatial data by applying AI algorithms, leading to improved spatial analysis, predictive modeling, and informed decision support.
Key Components:
- Geospatial Data Collection: GeoAI relies on diverse geospatial data sources, including remote sensing platforms, GPS, drones, and GIS databases. These data sources provide a wealth of information about the Earth’s surface, environment, and infrastructure.
- Artificial Intelligence Algorithms: AI algorithms, such as machine learning and deep learning, play a pivotal role in GeoAI. These algorithms learn from historical geospatial data to make predictions, classify objects, detect anomalies, and generate valuable insights.
Benefits:
- Enhanced Analysis: GeoAI enables the processing of large-scale geospatial datasets with speed and accuracy, leading to more insightful analyses and informed decision-making.
- Predictive Modeling: By identifying patterns and trends in historical data, GeoAI supports the creation of predictive models, helping organizations anticipate future scenarios and plan accordingly.
- Efficient Resource Allocation: In urban planning and disaster management, GeoAI assists in optimizing resource allocation by mapping high-risk areas and identifying infrastructure gaps.
- Environmental Monitoring: GeoAI aids in real-time monitoring of environmental parameters, such as air quality, deforestation rates, and ocean temperature, contributing to sustainability efforts.
Challenges:
- Data Complexity: Geospatial data can be complex, diverse, and voluminous, posing challenges in data preprocessing, integration, and feature extraction.
- Algorithm Selection: Selecting appropriate AI algorithms and models for specific geospatial tasks requires expertise and domain knowledge.
How is GeoAI Utilised?
GeoAI finds application across diverse industries and scenarios, addressing challenges and capitalising on opportunities.
| Application | Description |
| Government | Accelerates government services, predicts resource availability, and detects land-use changes. |
| Natural Resources | Transforms precision agriculture, monitors assets, and provides insights into tree volume. |
| National Mapping | Boosts productivity, speeds up GIS updates, and extracts data from big data. |
| Defence and Intelligence | Expedites data extraction, identifies entities, and assesses remote sensing data. |
| Public Safety | Improves public safety, predicts accidents, and identifies damaged infrastructure. |
| Insurance | Accelerates insurance claim processing, identifies damage, and facilitates recovery. |
| AEC (Architecture, Engineering, Construction) | Revolutionizes AEC, extracts insights from imagery, and enables energy-efficient designs. |
| Business Insights | Drives informed business decisions, provides market insights, and assesses new market viability. |
Geospatial Artificial Intelligence (GeoAI) has emerged as a game-changer across diverse sectors. Its capabilities in optimizing decision-making, enhancing environmental monitoring, and revolutionizing urban planning are evident through multifaceted applications. By effectively addressing contemporary challenges and offering data-driven insights, GeoAI stands as a transformative force that propels various sectors towards sustainable development and better living conditions.
Additional Information:
Random Forest Technology
- Random forest technology is a widely employed machine learning algorithm that amalgamates outcomes from multiple datasets to produce a final output.
- In the context of air quality prediction, researchers utilize historical data amassed from diverse air quality monitoring stations across a city.
- By employing the random forest algorithm, they forecast the Air Quality Index with enhanced accuracy and reliability.
More about the News:
- During the India Clean Air Summit (ICAS), an event focused on addressing air quality issues, the National Institute of Advanced Studies (NIAS), Bengaluru, introduced this innovative initiative.
- GeoAI employs artificial intelligence, satellite imagery, mobile technology, and citizen science to pinpoint sources of air pollution.
- This pilot project aims to evolve into a predictive tool for effectively monitoring air quality within the city. By utilizing historical data and employing conventional artificial neural network techniques, the project team develops a robust predictive model.
- The initiative operates within a comprehensive geospatial framework, integrating various data sources into a curated database. Beyond addressing air pollution, the project aligns with broader sustainable development goals (SDGs), encompassing issues like water pollution and electromagnetic radiation.
Yojna daily current affairs eng med 26th August 2023
Q1. With reference to GeoAI, consider the following statements:
- Geospatial artificial intelligence (GeoAI) combines artificial intelligence (AI) with geospatial data, science, and technology for enhanced decision-making.
- GeoAI relies solely on satellite imagery for geospatial data, excluding sources like GPS, drones, and GIS databases.
- GeoAI focuses on analysing historical data, without the capability to make future predictions or classify objects.
Which of the statements given above is/are correct?
(a) 1 only
(b) 2 and 3 only
(c) 3 only
(d) 1, 2 and 3
Answer: (a)
Q2. Consider the following:
- Predict accidents for improved public safety
- Damage identification and classification in Insurance
- Provides market insights
- Detects land-use changes
How many of the above are applications of geoAI?
(a) Only one
(b) Only two
(c) Only three
(d) All Four
Answer: (d)
Q3. Discuss the transformative impact of Geospatial Artificial Intelligence (GeoAI) in diverse sectors and its role in addressing contemporary challenges.
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