AI’s Role in Land Use Management
A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land Innovations – Data and Machine Learning".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 1697
Special Issue Editors
Interests: land management; land registration; cadastre; remote sensing; UAV; geospatial standards; GeoAI; sustainable development; GIS
Interests: spatial planning; infrastructure planning; citizen participation; land readjustment; land consolidation; real estate valuation; land register and cadaster
Interests: GeoAI; computer vision; feature extraction; point cloud processing; big geo-data; geospatial data science; natural language processing for geosocial analysis
Special Issue Information
Dear Colleagues,
The deep importance of AI for land use management focuses on AI's ability to provide the advanced insights and automation needed to address complex environmental and development pressures. Land is a finite and critical resource, and AI ensures that its management is not only reactive but also predictive, accurate, and sustainable.
The value of AI comes from two key functions: advanced geospatial analysis and predictive modeling. It uses machine learning algorithms to rapidly analyze data from multiple sources (satellite imagery, drone surveys, and sensor networks) to create high-resolution maps of current land conditions, detect subtle changes, and classify land cover with high accuracy.
The goal of this Special Issue is to collect papers (original research articles and review papers) to give insights about:
a) Conflict resolution and planning: providing unbiased data to planners and decision-makers to resolve boundary disputes, monitor compliance with zoning laws, and simulate the long-term impact of proposed infrastructure projects.
b) Climate resilience: by predicting the future susceptibility of land to climate threats (erosion, sea level rise, and desertification), AI enables relocation or reinforcement of resources from protecting vital agricultural land to safeguarding coastal infrastructure.
c) Resource efficiency: in sectors such as forestry and agriculture, AI optimizes land production by calculating the minimum inputs (water, nutrients) required for maximum yield, directly addressing issues related to resource scarcity and pollution through runoff.
d) Urban planning and land use: when it comes to urban development, AI models can simulate different growth scenarios, helping planners optimize zoning, infrastructure, and resource allocation to create more sustainable and resilient cities. They simplify processes such as cadastral mapping and land use compliance verification, improving data integrity.
The correlation between the role of AI in land use management and the Sustainable Development Goals (SDGs) is direct and transformative, primarily accelerating progress on environmental and infrastructure goals. AI provides the data-driven mechanism to translate SDG aspirations into concrete, measurable, effective, and scalable actions.
This Special Issue will welcome manuscripts that link the following themes:
| Thematic Area | AI & Advanced Technology Focus | Land Management & SDG Correlation |
|
GeoAI for Real-Time LULC Mapping and SDG Monitoring |
Focus: Developing and validating GeoAI/deep learning architectures for LULC mapping. Emphasis on rapid update cycles necessary for operational land management and disaster response. |
SDG 15 (Life on Land, tracking ecosystem health). Provides the crucial baseline data for all land-related SDGs (e.g., SDG 11, SDG 2). |
|
AI-Driven Climate Change Adaptation & Land Risk Mitigation |
Focus: Utilizing AI for predictive modeling of climate impacts (flood risk, drought, wildfire susceptibility, and pollution). Implementing Decision Support Systems (DSS) that suggest specific, risk-adjusted land management actions. |
SDG 13 (Climate Action, adaptation strategies). SDG 15.3 (Preventing land degradation). |
|
Smart Urban Growth and Sprawl Management via Digital Twins |
Focus: Creating Digital Twins of urban and peri-urban areas to simulate the impact of land use policies, zoning changes, and infrastructure projects before implementation. AI analyzes sprawl patterns and optimizes urban land allocation. |
SDG 11 (Sustainable Cities, managing spatial growth). SDG 9 (Industry, Innovation, and Infrastructure). |
|
AI for Resource Efficiency and Land Administration |
Focus: Integration of AI/ML with remote sensing for precision agriculture, cadastral mapping and land registration. Using AI to optimize resource allocation (water/fertilizer) and automate land ownership verification. |
SDG 2 (Zero Hunger, sustainable agriculture). SDG 6 (Clean Water). SDG 16 (Strong Institutions, transparent land records). |
|
Governance and Ethics of Land Management Digital Twins |
Focus: Investigating the policy, legal, and ethical frameworks required to govern the use of highly detailed, AI-fed Digital Twin models for public land management decisions. Ensuring equity and transparency in the simulation results. |
SDG 16 (Accountable institutions). SDG 10 (Reduced Inequalities). |
We look forward to receiving your original research articles and reviews.
Prof. Dr. Gheorghe Badea
Prof. Dr. Hans-Joachim Linke
Dr. Calimanut-Ionut Cira
Dr. Loredana Copăcean
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- GeoAI/Geospatial AI
- sustainable development goals (SDGs)
- climate resilience
- digital twin
- cadastral mapping/land registration
- LULC
- big data analytics
- computer vision
- remote sensing (RS)/satellite imagery analysis
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