We are pleased to highlight the article “Remote Sensing for Land Administration”, co-authored by our MLUMSE colleague Mila Koeva, together with Rohan Bennett and Claudio Persello, and published in GIM International.

The article examines how recent developments in remote sensing and geospatial information science can contribute to more efficient and fit-for-purpose land administration. It discusses the use of satellite imagery, airborne sensors, UAVs, Lidar, artificial intelligence, machine learning and automated feature extraction for the acquisition, processing and maintenance of land-related data.

Particular attention is given to the use of UAV imagery for cadastral mapping, including the influence of flight configuration, image overlap and ground control points on data quality. The authors also explore the growing role of GeoAI and deep learning in detecting visible cadastral boundaries, as well as the potential of high-resolution imagery and Lidar for building extraction and cadastral modernization.

At the same time, the article stresses that technology cannot replace human expertise and participation. Legal and socially recognized boundaries are not always visible in imagery, which means that consultation with landowners, local communities and land professionals remains essential.

The article provides a valuable overview of the opportunities and limitations of remote sensing in land administration and highlights its potential to support more accessible, efficient and sustainable land information systems.

Read the full article in GIM International:
https://www.gim-international.com/content/article/remote-sensing-for-land-administration