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 4
Special Issue Editors
Interests: GIS; geoAI; land use; agriculture; forestry; biodiversity
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
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Land is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
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
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
