A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Case studies on data-rich and data-poor countries

The aim of Work Package 5 is to assess the needs of decision-makers and end-users involved in the process of post-disaster recovery and to provide useful guidance, tools and recommendations for extracting information from the affected area to help with their decisions. This report follows from Deliverables D5.1 “Comparison of outcomes with end-user needs” and D5.2 “Semi-automated data extraction” where the team had set out to explore the needs of decision-makers and suggested protocols for tools to address their information requirements. This report begins with a summary of findings from the scenario planning game and a review of end-user priorities; it will then describe the methods of detecting post-disaster recovery evaluation and monitoring attributes to aid decision making. The proposed methods in the deliverables D2.6 “Supervised/Unsupervised change detection” and D5.2 “Semi-automated data extraction” for use in post-disaster recovery evaluation and monitoring are tested in detail for data-poor and data-rich scenarios. Semi-automated and automated methods of finding the recovery indicators pertaining to early recovery and monitoring are discussed. Step-by-step guidance for an analyst to follow in order to prepare the images and GIS data layers necessary to execute the semi-automated and automated methods are discussed in section 2. The outputs are presented in detail using case studies in section 3. In order to develop and assess the proposed detection methods, images from two case studies, namely Van in Turkey and Muzaffarabad in Pakistan, both recovering from recent earthquakes, have been used to highlight the differences between data-rich and data-poor countries and hence the constraints on outputs on the proposed methods

Breaking new ground in mapping human settlements from space -The Global Urban Footprint-

Today 7.2 billion people inhabit the Earth and by 2050 this number will have risen to around nine billion, of which about 70 percent will be living in cities. Hence, it is essential to understand drivers, dynamics, and impacts of the human settlements development. A key component in this context is the availability of an up-to-date and spatially consistent map of the location and distribution of human settlements. It is here that the Global Urban Footprint (GUF) raster map can make a valuable contribution. The new global GUF binary settlement mask shows a so far unprecedented spatial resolution of 0.4 arcsec ($\sim12 m$) that provides - for the first time - a complete picture of the entirety of urban and rural settlements. The GUF has been derived by means of a fully automated processing framework - the Urban Footprint Processor (UFP) - that was used to analyze a global coverage of more than 180,000 TanDEM-X and TerraSAR-X radar images with 3m ground resolution collected in 2011-2012. Various quality assessment studies to determine the absolute GUF accuracy based on ground truth data on the one hand and the relative accuracies compared to established settlements maps on the other hand, clearly indicate the added value of the new global GUF layer, in particular with respect to the representation of rural settlement patterns. Generally, the GUF layer achieves an overall absolute accuracy of about 85\%, with observed minima around 65\% and maxima around 98 \%. The GUF will be provided open and free for any scientific use in the full resolution and for any non-profit (but also non-scientific) use in a generalized version of 2.8 arcsec ($\sim84m$). Therewith, the new GUF layer can be expected to break new ground with respect to the analysis of global urbanization and peri-urbanization patterns, population estimation or vulnerability assessment