GenAI-Enabled Land Use Mapping as the Base for Modelling and Earth-Oriented Digital Twins
A special issue of Land (ISSN 2073-445X). This special issue belongs to the section "Land – Observation and Monitoring".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 308
Special Issue Editors
Interests: GeoAI; GeoLLM; spatial data infrastructures; geospatial interoperability; earth observation for sustainability; digital twins; semantic integration; spatial decision support for agriculture, forestry, climate adaptation and regional development
Special Issues, Collections and Topics in MDPI journals
Interests: land use; GIS; data mamangment
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Accurate, temporally consistent land-use/land-cover (LULC) information is a prerequisite for process-based environmental modeling and Earth-oriented Digital Twins. Recent progress in Generative Artificial Intelligence (GenAI)—including foundation models for vision, multimodal learning, and synthetic data generation—enables improved representation of heterogeneous landscapes, the fusion of multi-sensor data (optical, SAR, LiDAR), and systematic gap-filling in sparse time series. This Special Issue continues the previous “Land Use Mapping as the Base for Modeling and Earth-Oriented Digital Twins” and focuses on GenAI methods that increase mapping fidelity, update frequency, and the readiness of land products for downstream simulation, assessment, and decision support.
The aim of this Special Issue is to collect original research articles and reviews that (i) develop, benchmark, or rigorously assess GenAI approaches for LULC mapping and change detection, and (ii) demonstrate their added value for environmental modeling and Earth-oriented Digital Twins at local to global scales, in alignment with the journal’s scope on land systems, monitoring, and policy-relevant analytics.
Topics of interest include (this list is non-exhaustive):
- GenAI for semantic segmentation, instance mapping, change detection, and time-series reconstruction from EO data;
- Multimodal/multi-resolution fusion (EO, in-situ, socioeconomic, knowledge graphs, text);
- Synthetic data and augmentation; self-/semi-supervised pretraining under limited labels;
- Uncertainty quantification, calibration, and explainability for decision-relevant land products;
- Interoperable, reproducible pipelines delivering model-ready land data to Digital Twins;
- Benchmarking protocols and open datasets;
- Applications in agriculture, forestry, urban systems, hazards, biodiversity, and carbon accounting;
- Implications for governance and standards.
The Special Issue welcomes methodological advances, application studies, benchmark datasets, and systematic reviews.
Dr. Karel Charvat
Dr. Tomas Mildorf
Guest Editors
Manuscript Submission Information
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Keywords
- generative AI (GenAI)
- land-use/Land-cover (LULC) mapping
- earth observation (optical, SAR, LiDAR)
- multimodal data fusion
- earth-oriented digital twins
- uncertainty quantification and explainability
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