Advances in earth observation using unmanned aerial vehicles: a bibliometric and content analysis (2000–2024)

Unmanned Aerial Vehicle (UAV)-based Earth Observation, as a core technology for geospatial information acquisition, has profoundly reshaped data paradigms in fields like environmental monitoring, disaster response, and precision agriculture. This transformation is driven by its high resolution, flexible deployment, and multi-sensor collaboration capabilities. This paper reviews the technological evolution, current challenges, and future trends of UAV-based Earth Observation since 2000. Given the extensive and heterogeneous literature, we focus on the most relevant interdisciplinary research themes and their interactions. We analyzed 10,524 relevant publications; the country and journal contributing most to the field were China (2,950) and ISPRS Journal of Photogrammetry and Remote Sensing (3,400), respectively. Finally, we address critical future challenges:1) Technological breakthroughs (e.g. heterogeneous perception, multi-modal data fusion, knowledge-driven autonomous collection, and lightweight edge-AI models);2) Ethical governance (including privacy-sensitive data desensitization, fair/interpretable algorithm design, and low-altitude regulatory policy adaptation).We believe this analysis will inspire next-generation drone technology development and foster interdisciplinary innovation.

Authors

Institutions

Publication Details

Journal
Geocarto International
Published
2025-12-12
DOI
https://doi.org/10.1080/10106049.2025.2600779
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Advances in earth observation using unmanned aerial vehicles: a bibliometric and content analysis (2000–2024)

Shan Liao, Z. Chen, Kui Yang, KeWei Song et al.
Geocarto International
UAV Applications and Optimization
article

Advances in earth observation using unmanned aerial vehicles: a bibliometric and content analysis (2000–2024)

Shan Liao, Z. Chen, Kui Yang, KeWei Song, Haixing Shang, Zhang Zhao, Zufeng Li, Yan Ma, Chao Zhan, Emily S. Shen
article en

Abstract

Unmanned Aerial Vehicle (UAV)-based Earth Observation, as a core technology for geospatial information acquisition, has profoundly reshaped data paradigms in fields like environmental monitoring, disaster response, and precision agriculture. This transformation is driven by its high resolution, flexible deployment, and multi-sensor collaboration capabilities. This paper reviews the technological evolution, current challenges, and future trends of UAV-based Earth Observation since 2000. Given the extensive and heterogeneous literature, we focus on the most relevant interdisciplinary research themes and their interactions. We analyzed 10,524 relevant publications; the country and journal contributing most to the field were China (2,950) and ISPRS Journal of Photogrammetry and Remote Sensing (3,400), respectively. Finally, we address critical future challenges:1) Technological breakthroughs (e.g. heterogeneous perception, multi-modal data fusion, knowledge-driven autonomous collection, and lightweight edge-AI models);2) Ethical governance (including privacy-sensitive data desensitization, fair/interpretable algorithm design, and low-altitude regulatory policy adaptation).We believe this analysis will inspire next-generation drone technology development and foster interdisciplinary innovation.

Geocarto InternationalVol. 40(1)
Wuhan University (CN), PowerChina (China) (CN), China Power Engineering Consulting Group (China) (CN), Henan Polytechnic University (CN)
Openalex Percentile: Top 49%
UAV Applications and Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.