Large-Scale LULC Mapping on Google Earth Engine (GEE)
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: 31 December 2025 | Viewed by 36
Special Issue Editors
Interests: GNSS; remote sensing; photogrammetry; UAV; LULC
Interests: GNSS; remote sensing; GIS
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; LULC; GEE; GIS
Special Issue Information
Dear Colleagues,
Land use and land cover (LULC) mapping plays a crucial role in a wide range of disciplines, from environmental monitoring and natural resource management to urban planning and climate change studies. In recent years, the availability of satellite imagery and the advancement of cloud computing platforms such as Google Earth Engine (GEE) have revolutionized the ability to perform large-scale LULC mapping efficiently and at unprecedented spatial and temporal scales. GEE offers access to vast geospatial datasets and powerful analytical tools, making it possible for researchers and practitioners worldwide to develop accurate and timely LULC products. The scientific community continues to seek innovative methods, datasets, and workflows that further enhance the accuracy, automation, and reproducibility of LULC mapping at regional, national, and global scales.
The goal of this Special Issue is to collect papers (original research articles and review papers) that provide new insights into the methods, challenges, and applications of large-scale LULC mapping using Google Earth Engine. This Special Issue aims to showcase recent advancements in algorithm development, validation strategies, the integration of multi-source data, and operational applications. Contributions that address both theoretical and practical aspects of LULC mapping are encouraged. The Special Issue aligns with the journal’s scope by advancing knowledge in geospatial analysis, remote sensing, environmental monitoring, and applied computing technologies.
This Special Issue will welcome manuscripts that link the following themes:
- The development of innovative LULC classification methods using GEE;
- Applications of machine learning and deep learning for large-scale LULC mapping;
- The integration of multi-temporal and multi-sensor data for improved LULC mapping;
- Accuracy assessment, validation, and uncertainty quantification strategies;
- Case studies demonstrating operational LULC mapping projects;
- Reviews and perspectives on current trends and future directions in LULC mapping with GEE.
We look forward to receiving your original research articles and reviews.
Dr. Matteo Cutugno
Dr. Umberto Robustelli
Dr. Yasir Hassan Khachoo
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
- LULC
- GEE
- remote sensing
- deep learning
- machine learning
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