A GEOSPATIAL ARTIFICIAL INTELLIGENCE-INTERNET OF THINGS (GEOAI-IOT) BASED EARLY WARNING LAND HEALTH MONITORING SYSTEM

Abstract

The present invention relates to a Geospatial Artificial Intelligence-Internet of Things (GeoAI-IoT) based early warning system designed to assess and monitor land health, providing real-time data on soil quality, vegetation cover, and environmental degradation. The system comprises of: a network of IoT sensors to gather data on multiple parameters such as soil moisture, air quality, temperature, and humidity; drones equipped with multispectral and thermal sensors to capture high-resolution aerial imagery; a centralized geospatial database to receive the data; advanced Geospatial AI algorithm to identify potential land degradation patterns, water body contamination, and vegetation loss; and a user-friendly interface for featuring real-time data visualization and automated alert systems. Traditional monitoring methods are often time-consuming, labor-intensive, and provide delayed results, which limits effective decision-making. By combining advanced GeoAI algorithms with IoT sensors, this system enables the continuous collection and analysis of spatial data, offering precise and timely insights into the environmental impacts of mining activities. The IoT network, comprising ground sensors and aerial drones, feeds real-time data into a GeoAI platform that applies machine learning techniques to predict potential risks and trigger early warnings. The system facilitates proactive land management by providing actionable intelligence to policymakers and environmental agencies, allowing for mitigation strategies before irreversible damage occurs. The proposed GeoAI-IoT framework addresses critical gaps in current environmental monitoring systems by offering scalable, automated, and cost-effective solutions for sustainable land management in mining regions. This research contributes to the advancement of digital technologies for environmental health monitoring, ultimately supporting efforts to achieve sustainable development and environmental conservation.

Specification

Description:Field of the invention
The present invention relates in general to geographic information system, more particular to a Geospatial Artificial Intelligence-Internet of Things (GeoAI-IoT) based early warning system designed to assess and monitor land health, providing real-time data on soil quality, vegetation cover, and environmental degradation.
Background of the Invention
The primary problem addressed by this invention is the inefficiency and limitations of traditional land health monitoring methods, particularly in opencast mining regions. Opencast mining leads to severe environmental degradation, including soil erosion, deforestation, biodiversity loss, water pollution, and landscape changes. Monitoring these impacts in real-time is crucial for effective land management and mitigating long-term environmental damage. Conventional approaches rely on manual sampling, satellite imagery, and ground-based surveys, which are often time-consuming, expensive, and lack the capacity for continuous data collection and analysis. This delay in detecting environmental degradation hinders timely intervention, resulting in irreversible damage.
The GeoAI-IoT-based system solves this problem by integrating artificial intelligence, geospatial data, and IoT sensors to create an automated, real-time land health monitoring platform. It enables continuous, high-resolution monitoring of soil quality, vegetation cover, water bodies, and land-use changes, providing early warnings of environmental risks and allowing for proactive decision-making. The invention provides a more scalable, cost-effective, and responsive solution compared to traditional methods.
Existing research highlights the importance of real-time monitoring systems in environmental health management. IoT-based sensor networks have been explored in various applications, including agricultural monitoring, urban infrastructure, and smart cities. However, their integration with geospatial AI for environmental monitoring in mining regions remains limited. Several studies have used remote sensing and geographic information systems (GIS) for land-use monitoring, but these approaches face challenges in terms of resolution, frequency of data collection, and processing times. For instance, satellite-based methods provide broad coverage but are often constrained by weather conditions, and revisits are infrequent. Similarly, manual field surveys are accurate but labor-intensive and localized.
Machine learning and AI-driven approaches have also been applied to environmental data for predictive analytics. These techniques enable better identification of patterns and trends in environmental degradation. However, most studies focus on either AI or IoT in isolation, lacking a cohesive system that integrates both technologies for dynamic, real-time monitoring.
The proposed GeoAI-IoT-based system stands out by merging the strengths of AI, geospatial technology, and IoT into a unified framework, offering a comprehensive and automated solution for land health monitoring in opencast mining regions. Key differentiators include:

a) Real-Time Monitoring: Unlike traditional systems, this invention uses IoT sensors deployed across the mining area to collect continuous data on environmental parameters like soil moisture, air quality, and vegetation health. The data is processed in real time by GeoAI algorithms, enabling immediate detection of risks and early warnings.
b) Integrated GeoAI for Predictive Analytics: The system applies machine learning and deep learning models to spatial data, allowing for predictive insights into land degradation patterns, environmental risks, and future impacts. This goes beyond current GIS-based monitoring, which primarily focuses on descriptive analysis.
c) High Precision and Scalability: Combining IoT with drone-based remote sensing allows the system to cover large areas with high spatial resolution and precision, which overcomes the limitations of satellite imagery. Additionally, the system is scalable and can be adapted to different environments, making it suitable for various mining regions worldwide.
d) Proactive Environmental Management: By providing early warnings based on predictive models, the system enables proactive land management strategies. It allows policymakers and environmental agencies to implement mitigation measures, such as reforestation, erosion control, and pollution management, before the damage becomes irreversible.
This invention offers a significant leap forward in addressing the environmental challenges posed by opencast mining, enabling sustainable land management through cutting-edge digital technologies.

Drawings
Figure 1 Illustration of graphical representation: a) Soil moisture over time, b) Air quality index over time, c) Vegetation health over time, and d) Predicted erosion risk over time.
Figure 2 illustrates the present system methodoly
Figure 3 Illustrations of obtained results: a) IoT Sensor Data Over Time, b) NDVI vs Surface Erosion, and GeoAI Predictive Model Results.
Figure 4 Graphical representation of leveraging GeoAI-IoT-based early warning land health monitoring system and assessment in the scope of opencast mining regions
Detailed Description of the Invention
The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.
In any embodiment described herein, the open-ended terms "comprising," "comprises," and the like (which are synonymous with "including," "having" and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like. As used herein, the singular forms "a", "an", and "the" designate both the singular and the plural, unless expressly stated to designate the singular only.
The present GeoAI-IoT-based system solved the above mentioned problem by integrating artificial intelligence, geospatial data, and IoT sensors to create an automated, real-time land health monitoring platform.
The GeoAI-IoT-based system comprises of: a network of IoT sensors to gather data on multiple parameters such as soil moisture, air quality, temperature, and humidity; drones equipped with multispectral and thermal sensors to capture high-resolution aerial imagery; a centralized geospatial database to receive the data; advanced Geospatial AI algorithm to identify potential land degradation patterns, water body contamination, and vegetation loss; and a user-friendly interface for featuring real-time data visualization and automated alert systems.
Figure 2 shows a methodology that includes IoT sensors and drones for data collection and monitoring environmental parameters like air quality and soil moisture. Geospatial data is processed using GeoAI for real-time predictive analysis. An early warning system and a user-friendly dashboard display critical environmental data, generating automated alerts and reports for decision-making. The method to assess and monitor land health comprising the following steps:
a) Data Collection Using IoT Sensors and Drones
• IoT Network Setup: A network of IoT sensors was deployed across various environmental monitoring points within the opencast mining region. These sensors gathered data on multiple parameters such as soil moisture, air quality, temperature, and humidity.
• Drone-Assisted Data Acquisition: Drones equipped with multispectral and thermal sensors were used to capture high-resolution aerial imagery. This imagery was further processed to extract information on vegetation health, surface erosion, and topographic changes.
b) Geospatial Data Processing and Analysis
• Spatial Data Fusion: Data collected from IoT sensors and drones were integrated into a centralized geospatial database, creating a multi-layered environmental dataset.
• GeoAI-Based Analytics: Using advanced Geospatial AI algorithms, predictive models were developed to identify potential land degradation patterns, water body contamination, and vegetation loss. The system utilized both supervised and unsupervised machine learning techniques to assess these patterns in real-time.
c) Early Warning System Development
• Predictive Modelling: GeoAI models were trained on historical land health data and environmental factors, enabling early detection of potential risks (such as soil erosion and air pollution). These predictive models were validated using historical datasets and compared to real-time sensor data for accuracy.
• Risk Classification: A risk classification model was implemented to categorize areas into zones of high, medium, and low environmental risk, allowing for prioritization of preventive actions.
d) User Interface and Visualization
• Dashboard Creation: A user-friendly interface was developed, featuring real-time data visualization and automated alert systems. The interface displayed critical environmental parameters, including land degradation indices, vegetation health scores, and real-time environmental alerts.
• Custom Alerts and Reports: Based on threshold values for specific environmental parameters, the system generated early warnings and automated reports, assisting decision-makers in taking preventive action before irreversible environmental damage occurred.
Wherein the GeoAI-IoT-based system analysis are as follows:
a) IoT Sensor Data:
1. Air Quality (PM2.5 Concentrations): Air quality was monitored hourly across multiple points, with particulate matter (PM2.5) concentrations increasing from 30 µg/m³ to 120 µg/m³. The values indicate a steady degradation in air quality, especially as PM2.5 levels exceeded 85 µg/m³, posing potential health risks according to environmental standards.
2. Soil Moisture (%): Soil moisture content was measured hourly, showing an incremental rise from 15% to 35%. The data reflect moisture retention trends in different areas, with variations influenced by factors such as precipitation, evaporation, and vegetation cover.
3. Temperature (°C): Hourly temperature readings indicate a progressive increase from 22°C to 35°C, consistent with daytime warming. These temperature fluctuations may influence both soil moisture levels and evapotranspiration rates in the region.
4. Humidity (%): Relative humidity levels were recorded between 45% and 80%, with higher humidity observed in areas with increased soil moisture, suggesting localized interactions between temperature, soil conditions, and air moisture content.
b) Drone-Assisted Imagery Data:
1. Vegetation Health (NDVI Index): The NDVI values, ranging from 0.2 to 0.6, highlight variability in vegetation health across the opencast mining region. Zones with NDVI values below 0.3 suggest degraded vegetation cover, likely impacted by mining activities, while higher values around 0.5 indicate relatively healthy vegetation zones.
2. Surface Erosion (mm/day): Erosion rates, derived from topographic analysis of drone imagery, ranged from 0.5 mm/day to 3 mm/day. These rates suggest varying levels of soil displacement, with higher erosion in areas where vegetation is sparse, correlating with lower NDVI values.
3. Topographic Changes (Elevation Differences): Elevation shifts ranging from 0.3 m to 1.5 m indicate significant terrain modifications due to both natural and anthropogenic activities. Zones with more pronounced elevation changes align with areas undergoing surface erosion or mining disturbances.
c) GeoAI-Based Predictive Analytics:
1. Land Degradation Prediction: GeoAI models estimate a high probability (0.5 to 0.9) of land degradation in certain zones. Areas with probabilities above 0.8 are identified as critical zones, requiring immediate intervention to mitigate further environmental deterioration.
2. Water Body Contamination: Using unsupervised machine learning clustering, the system estimates water contamination probabilities between 0.2 and 0.5. Higher contamination risk (0.5) is observed near zones with elevated land degradation and surface erosion rates, indicating potential runoff and pollution pathways.
3. Vegetation Loss Prediction: The GeoAI models predict vegetation loss ranging from 5% to 25% across various areas. Zones with the highest predicted loss (25%) correlate strongly with elevated land degradation probabilities and lower NDVI values, further emphasizing the need for targeted restoration efforts.
This comprehensive analysis, integrating IoT and drone data with GeoAI-based predictions, highlights critical environmental risks in the mining region, providing actionable insights for timely mitigation strategies.
The graph shown in Figure 1., along with the system's real-time and predictive capabilities, demonstrates how the GeoAI-IoT Early Warning Land Health Monitoring System offers a groundbreaking solution for environmental monitoring in opencast mining regions. It delivers a cost-effective, scalable, and highly accurate approach to managing land health while promoting sustainability and regulatory compliance.
1. Soil Moisture Over Time:
o Real-time monitoring of soil moisture data reveals fluctuations between 20%-60% across a 12-month period, allowing for timely intervention before soil degradation worsens.
2. Vegetation Health Over Time:
o Continuous analysis shows vegetation health varying between 50%-100%, which can be addressed before irreversible damage occurs.
3. Predicted Erosion Risk:
o Predictive models indicate erosion risk fluctuating from 10%-90% over the course of the year, allowing for proactive land management measures.
4. Air Quality Index:
o Air quality fluctuates between 50 and 150, with timely alerts helping reduce potential air pollution impacts on nearby communities.
The plots shown in Figure 3 (a-c), visualize data from the obtained results:
a) IoT Sensor Data Over Time: Air quality, soil moisture, temperature, and humidity trends across time show significant variations, especially in air quality, which worsens over time.
b) NDVI vs Surface Erosion: A scatter plot demonstrates the correlation between vegetation health (NDVI) and surface erosion. Lower NDVI values correspond to higher surface erosion rates, suggesting vegetation loss increases erosion risks.
c) GeoAI Predictive Model Results: The bar chart illustrates the predicted probabilities of land degradation and water contamination across various zones, with some zones showing higher environmental risks than others.
This system leverages the power of GeoAI-IOT to provide precise, predictive, and cost-effective environmental monitoring, wherein
• the GeoAI-IoT system reduces operational costs by up to 40%. This savings is primarily due to automation and reduced need for human resources.
• Real-time data showed a gradual increase in soil erosion risk from 20% to 60% over three months. Based on early warning alerts, remedial measures (such as soil stabilization techniques) were implemented, halting further degradation.
• Predicted erosion risk levels in an opencast mining site using GeoAI-based analysis were found to be 85% accurate when compared to actual field observations over a 12-month period.
• In a pilot project, early warning alerts led to a 30% reduction in the environmental footprint of mining operations over a one-year period; and
• Traditional monitoring methods can be significantly more expensive than mining operations, but the GeoAI-IoT system reduces these costs through automation and decreased labour requirements.

, Claims:1. A GeoAI-IoT-based system to assess and monitor land health, comprises of:
a) a network of IoT sensors to gather data on multiple parameters such as soil moisture, air quality, temperature, and humidity;
b) drones equipped with multispectral and thermal sensors to capture high-resolution aerial imagery;
c) a centralized geospatial database to receive the data;
d) advanced Geospatial AI algorithm to identify potential land degradation patterns, water body contamination, and vegetation loss; and
e) a user-friendly interface for featuring real-time data visualization and automated alert systems.
2. The GeoAI-IoT-based system to assess and monitor land health as clime in the claim 1, wherein the method to assess and monitor land health comprising the following steps:
Step 1: Data Collection Using IoT Sensors and Drones
• These sensors gathered data on multiple parameters such as soil moisture, air quality, temperature, and humidity.
• Drones equipped with multispectral and thermal sensors were used to capture high-resolution aerial imagery.
Step 2: Geospatial Data Processing and Analysis
• Data collected from IoT sensors and drones were integrated into a centralized geospatial database, creating a multi-layered environmental dataset.
• Using advanced Geospatial AI algorithms, predictive models were developed to identify potential land degradation patterns, water body contamination, and vegetation loss.
Step 3: Early Warning System Development
• GeoAI models were trained on historical land health data and environmental factors, enabling early detection of potential risks (such as soil erosion and air pollution). These predictive models were validated using historical datasets and compared to real-time sensor data for accuracy.
• A risk classification model was implemented to categorize areas into zones of high, medium, and low environmental risk, allowing for prioritization of preventive actions.
Step 4: User Interface and Visualization
• A user-friendly interface displayed critical environmental parameters, including land degradation indices, vegetation health scores, and real-time environmental alerts.
• the system generated early warnings and automated reports, assisting decision-makers in taking preventive action before irreversible environmental damage occurred based on threshold values for specific environmental parameters,.
3. The GeoAI-IoT-based system to assess and monitor land health as clime in the claim 1, wherein system shows the results are as follows:
• the GeoAI-IoT system reduces operational costs by up to 40%. This savings is primarily due to automation and reduced need for human resources.
• Real-time data showed a gradual increase in soil erosion risk from 20% to 60% over three months. Based on early warning alerts, remedial measures (such as soil stabilization techniques) were implemented, halting further degradation.
• Predicted erosion risk levels in an opencast mining site using GeoAI-based analysis were found to be 85% accurate when compared to actual field observations over a 12-month period.
• In a pilot project, early warning alerts led to a 30% reduction in the environmental footprint of mining operations over a one-year period; and
• Traditional monitoring methods can be significantly more expensive than mining operations, but the GeoAI-IoT system reduces these costs through automation and decreased labour requirements.
4. The GeoAI-IoT-based system to assess and monitor land health as clime in the claim 1, wherein
• GeoAI models estimate a high probability (0.5 to 0.9) of land degradation in certain zones. Areas with probabilities above 0.8 are identified as critical zones, requiring immediate intervention to mitigate further environmental deterioration;
• Using unsupervised machine learning clustering, the system estimates water contamination probabilities between 0.2 and 0.5. Higher contamination risk (0.5) is observed near zones with elevated land degradation and surface erosion rates, indicating potential runoff and pollution pathways; and
• The GeoAI models predict vegetation loss ranging from 5% to 25% across various areas. Zones with the highest predicted loss (25%) correlate strongly with elevated land degradation probabilities and lower NDVI values.

Documents