ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
Scope & Guideline
Transforming Data into Knowledge in Geospatial Sciences
Introduction
Aims and Scopes
- Remote Sensing Techniques and Applications:
The journal covers a wide range of remote sensing techniques, including satellite, aerial, and UAV-based methods, focusing on their applications in environmental monitoring, urban planning, agriculture, and disaster management. - Photogrammetry Innovations:
Research related to photogrammetry, including 3D modeling, structure from motion, and image processing techniques, is a key focus area, emphasizing advancements in methodology and technology. - Data Fusion and Integration:
The journal emphasizes the integration of various data sources, such as combining optical, radar, and LiDAR data, to enhance the accuracy and richness of geospatial information. - Machine Learning and AI Applications:
There is a strong emphasis on the application of machine learning and artificial intelligence techniques in remote sensing, particularly for classification, change detection, and object recognition. - Environmental and Agricultural Monitoring:
Research focusing on the use of remote sensing for monitoring environmental changes, agricultural practices, and land use dynamics is a significant part of the journal's scope. - Urban Remote Sensing:
The journal addresses urban studies through remote sensing, including urban heat islands, land cover classification, and infrastructure monitoring.
Trending and Emerging
- AI and Machine Learning in Remote Sensing:
There is a significant increase in research focusing on the application of AI and machine learning in remote sensing, particularly for tasks like image classification, change detection, and data fusion. - Multi-Source Data Fusion:
Emerging studies increasingly emphasize the integration of data from multiple sources, such as combining satellite, UAV, and ground-based sensors, to enhance analysis and improve accuracy. - Real-Time Remote Sensing Applications:
Research is trending towards real-time applications of remote sensing data, particularly in environmental monitoring and disaster response, highlighting the need for rapid data processing and analysis. - Urban Remote Sensing and Smart Cities:
The focus on urban applications of remote sensing is growing, with increased interest in monitoring urban dynamics, infrastructure, and environmental impacts in the context of smart city initiatives. - Phenological Studies and Climate Monitoring:
An emerging trend is the use of remote sensing for detailed studies of phenology and climate change impacts, contributing to a better understanding of ecosystem dynamics.
Declining or Waning
- Traditional Photogrammetry:
As new technologies and methodologies emerge, traditional photogrammetry techniques are becoming less emphasized in favor of more advanced methods such as machine learning and AI-driven approaches. - Basic Remote Sensing Algorithms:
The focus on basic algorithms for remote sensing data processing is waning as researchers increasingly adopt complex, data-driven models that leverage deep learning and other advanced techniques. - Single-Sensor Studies:
Research that focuses solely on single-sensor studies is declining, with a growing preference for multi-sensor approaches that provide more comprehensive insights. - Manual Data Processing Techniques:
There is a noticeable reduction in publications discussing manual or semi-automated data processing techniques, as the field moves towards fully automated and AI-driven solutions.
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