
Rana Waqar Aslam
Anhui Normal University; Wuhan University
Dr. Rana Waqar Aslam is a remote sensing and geospatial data scientist with over a decade of professional experience in GIS, Earth observation, and environmental modeling across Pakistan, the United Arab Emirates, the United Kingdom, and China. He obtained his PhD in photogrammetry and remote sensing from Wuhan University, a globally leading institution in this field. Dr. Aslam has published more than 50 peer-reviewed articles in high-impact journals, with an h-index of 29 and over 2,500 citations. His research focuses on multi-sensor data integration, machine learning and deep learning applications, GeoAI, and cloud-based geospatial analysis for environmental monitoring. He has developed innovative frameworks for wetland mapping, climate-driven vulnerability assessment, and large-scale Earth system analysis. In addition, he actively contributes to the scientific community as an associate editor, guest editor, and reviewer for numerous international journals.

Iram Naz
Wuhan University
Iram Naz is a remote sensing and GIS researcher currently pursuing her PhD in cartography and geographical information engineering at Wuhan University, China. She holds an MSc in Water Resources Management and a bachelor’s degree in environmental sciences. Her research integrates remote sensing, geospatial analysis, and machine learning to address environmental and hydrological challenges. She has contributed to multiple peer-reviewed publications in reputable journals, focusing on groundwater quality assessment, wetland dynamics, geohazard vulnerability, and climate-driven environmental changes. Her work emphasizes multi-source data integration, geostatistical modeling, and cloud-based geospatial platforms such as Google Earth Engine. With professional experience as a remote sensing/GIS analyst and environmental expert, she brings practical and research-driven insights into sustainable environmental monitoring and decision-making.
This Topical Collection focuses on advanced and reproducible remote sensing workflows for comprehensive Earth system monitoring, integrating artificial intelligence (AI), machine learning (ML), deep learning, geospatial artificial intelligence (GeoAI), and cloud computing platforms. With the rapid growth of satellite missions and open-access geospatial data, remote sensing has become a cornerstone for analyzing complex environmental and societal challenges across climate change, urbanization, disaster risk, air quality, water resources, wetlands, soil systems, and ecological dynamics.
The importance of this topic lies in the growing need for scalable, automated, and transferable methodologies that can efficiently process large volumes of multi-source satellite data. Emerging technologies such as deep learning models, cloud-based platforms like Google Earth Engine, and multi-sensor data fusion approaches are transforming traditional remote sensing into a dynamic, real-time decision-support system. However, the lack of standardized and reproducible workflows often limits the broader applicability of these methods.
This collection aims to provide a comprehensive platform for researchers to present step-by-step, video-based methodologies that demonstrate the integration of remote sensing data with AI-driven analytical techniques. It will highlight innovative frameworks for multi-source data fusion, environmental modeling, disaster monitoring, urban growth analysis, and ecosystem assessment. By emphasizing reproducibility, transparency, and methodological rigor, this collection will support the scientific community in developing scalable solutions for global environmental challenges. Ultimately, it seeks to bridge the gap between advanced geospatial technologies and practical applications, fostering interdisciplinary collaboration and accelerating scientific innovation in Earth observation.
Long-term CES assessment using historical POI data: A cross-scale study of heritage sites in Southern Anhui
FuQiang Tan1,
Yihan Feng2,
Feng Qin*3
1Research Center for Culture and Health Behavior, Huainan Normal University,
2Industrial Digital Research Centre, Huainan Normal University,
3Huizhou Culture Research Center, Anhui Normal University
Carbon Storage Under Multi-Scenario Land Use Projections: A PLUS-InVEST-OPGD Assessment in the Yixian-Huangshan World Heritage Landscape, China
Mengke Luo1
1Macau University of Science and Technology
Safeguarding Intangible Cultural Heritage through Digital Storytelling and Geospatial Technologies: A Case Study of Zhaoxing Dong Village, China
Komal Mehreen1,
Intikhab Ahmad*1,
Feng Qin*1
1Anhui Normal University
Investigation of Morphometric Indices Through Hypsometry, Isobase and Relative Relief: Inferences from Remote Sensing and GIS
Syed Amer Mahmood*1
1Institute of Space Science, University of the Punjab
Cloud-Based Remote Sensing of Surface Water Dynamics and Agricultural Water Resource Sustainability in Punjab, Pakistan
Fahad Ali1,
Muhammad Sohail Amjad Makhdum*2,
Faheem Raza Shamsi1,
Magdalena Radulescu3,
Razvan Serbu3,
Bogdan Marza3
1Department of Geography, GIS and Remote Sensing, Government College University Faisalabad, Faisalabad 38000, Pakistan,
2Department of Economics, Government College University Faisalabad, Faisalabad, 38000, Pakistan,
3Lucian Blaga University of Sibiu, Romania
Seasonal Spatiotemporal Dynamics of Land Surface Temperature Using Spectral Indices based on Google Earth Engine and Polynomial Regression
Muhammad Sajid Mehmood1
1Pingdingshan University
Wetland Changes and Cultural Ecosystem Services in the Huizhou Protection Zone: A Multi-Source Remote Sensing Analysis (2008–2024)
Ju Bai1
1Huainan Normal University Cultural Heritage Research Center
Assessing Human Activity & Law Effectiveness in Anhui’s Ecological Redlines via Remote Sensing (2018–2025)
Wei Li*1
1Industrial Digital Research Centre, Huainan Normal University
Assessing Graduate Employment Quality Using Nighttime Light and Remote Sensing: A Spatial Analytical Approach
Xu Zhang*1
1Industrial Digital Research Centre, Huainan Normal University
Distribution, Drivers and Viewing‑Suitability of Red Heritage Sites in Guangdong via Multi‑Source Remote‑Sensing and Geodetector
Yanxia Wang*1
1Huainan Normal University