Synthetic Aperture Radar (SAR) Meets Deep Learning

This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Semantic location extraction from crowdsourced data

Crowdsourced Data (CSD) has recently received increased attention in many application areas including disaster management. Convenience of production and use, data currency and abundancy are some of the key reasons for attracting this high interest. Conversely, quality issues like incompleteness, credibility and relevancy prevent the direct use of such data in important applications like disaster management. Moreover, location information availability of CSD is problematic as it remains very low in many crowd sourced platforms such as Twitter. Also, this recorded location is mostly related to the mobile device or user location and often does not represent the event location. In CSD, event location is discussed descriptively in the comments in addition to the recorded location (which is generated by means of mobile device's GPS or mobile communication network). This study attempts to semantically extract the CSD location information with the help of an ontological Gazetteer and other available resources. 2011 Queensland flood tweets and Ushahidi Crowd Map data were semantically analysed to extract the location information with the support of Queensland Gazetteer which is converted to an ontological gazetteer and a global gazetteer. Some preliminary results show that the use of ontologies and semantics can improve the accuracy of place name identification of CSD and the process of location information extraction