Generalized Sparse Convolutional Neural Networks for Semantic Segmentation of Point Clouds Derived from Tri-Stereo Satellite Imagery

We studied the applicability of point clouds derived from tri-stereo satellite imagery for semantic segmentation for generalized sparse convolutional neural networks by the example of an Austrian study area. We examined, in particular, if the distorted geometric information, in addition to color, influences the performance of segmenting clutter, roads, buildings, trees, and vehicles. In this regard, we trained a fully convolutional neural network that uses generalized sparse convolution one time solely on 3D geometric information (i.e., 3D point cloud derived by dense image matching), and twice on 3D geometric as well as color information. In the first experiment, we did not use class weights, whereas in the second we did. We compared the results with a fully convolutional neural network that was trained on a 2D orthophoto, and a decision tree that was once trained on hand-crafted 3D geometric features, and once trained on hand-crafted 3D geometric as well as color features. The decision tree using hand-crafted features has been successfully applied to aerial laser scanning data in the literature. Hence, we compared our main interest of study, a representation learning technique, with another representation learning technique, and a non-representation learning technique. Our study area is located in Waldviertel, a region in Lower Austria. The territory is a hilly region covered mainly by forests, agriculture, and grasslands. Our classes of interest are heavily unbalanced. However, we did not use any data augmentation techniques to counter overfitting. For our study area, we reported that geometric and color information only improves the performance of the Generalized Sparse Convolutional Neural Network (GSCNN) on the dominant class, which leads to a higher overall performance in our case. We also found that training the network with median class weighting partially reverts the effects of adding color. The network also started to learn the classes with lower occurrences. The fully convolutional neural network that was trained on the 2D orthophoto generally outperforms the other two with a kappa score of over 90% and an average per class accuracy of 61%. However, the decision tree trained on colors and hand-crafted geometric features has a 2% higher accuracy for roads

Prevalence and Severity of Burn Scars in Rural Mozambique

BackgroundBurn injuries are common in low- and middle-income countries (LMICs) and their associated disability is tragic. This study is the first to explore burn scars in rural communities in Mozambique. This work also validated an innovate burn assessment tool, the Morphological African Scar Contractures Classification (MASCC), used to determine surgical need.MethodsUsing a stratified, population-weighted survey, the team interviewed randomly selected households from September 2012 to June 2013. Three rural districts (Chókwè, Nhamatanda, and Ribáuè) were selected to represent the southern, central and northern regions of the country. Injuries were recorded, documented with photographs, and approach to care was gathered. A panel of residents and surgeons reviewed the burn scar images using both the Vancouver Scar Scale and the MASCC, a validated visual scale that categorizes patients into four categories corresponding to levels of surgical intervention.ResultsOf the 6104 survey participants, 6% (n = 370) reported one or more burn injuries. Burn injuries were more common in females (57%) and most often occurred on the extremities. Individuals less than 25 years old had a significantly higher odds of reporting a burn scar compared to people older than 45 years. Based on the MASCC, 12% (n = 42) would benefit from surgery to treat contractures.ConclusionUntreated burn injuries are prevalent in rural Mozambique. Our study reveals a lack of access to surgical care in rural communities and demonstrates how the MASCC scale can be used to extend the reach of surgical assessment beyond the hospital through community health workers

Soil Moisture Remote Sensing: State-of-the-Science

This is an update to the special section “Remote Sensing for Vadose Zone Hydrology—A Synthesis from the Vantage Point” [Vadose Zone Journal 12(3)]. Satellites (e.g., Soil Moisture Active Passive [SMAP] and Soil Moisture and Ocean Salinity [SMOS]) using passive microwave techniques, in particular at L-band frequency, have shown good promise for global mapping of near-surface (0–5-cm) soil moisture at a spatial resolution of 25 to 40 km and temporal resolution of 2 to 3 d. C- and X-band soil moisture records date back to 1978, making available an invaluable data set for long-term climate research. Near-surface soil moisture is further extended to the root zone (top 1 m) using process-based models and data assimilation schemes. Validation of remotely sensed soil moisture products has been ongoing using core monitoring sites, sparse monitoring networks, intensive field campaigns, as well as multi-satellite comparison studies. To transfer empirical observations across space and time scales and to develop improved retrieval algorithms at various resolutions, several efforts are underway to associate soil moisture variability dynamics with land surface attributes in various energy- and water-rich environments. We describe the most recent scientific and technological advances in soil moisture remote sensing. We anticipate that remotely sensed soil moisture will find many applications in vadose zone hydrology in the coming decades

White Book on Physical and Rehabilitation Medicine in Europe Introductions, Executive Summary, and Methodology

The White Book (WB) of Physical and Rehabilitation Medicine (PRM) in Europe is produced by the 4 European PRM Bodies (European Academy of Rehabilitation Medicine - EARM, European Society of PRM - ESPRM, European Union of Medical Specialists - PRM Section, European College of PRM-ECPRM served by the European Union of Medical Specialists-PRM Board) and constitutes the reference book for PRM physicians in Europe. It has now reached its third edition; the first was published in 1989 and the second in 2006/2007. The WB has multiple purposes, including providing a unifying framework for European countries, to inform decision-makers on European and national level, to offer educational material for PRM trainees and physicians and information about PRM to the medical community, other rehabilitation professionals and the public