There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.

/pdf/evaluation-of-different-machine-learning-methods-and-deep-5rqqhxf2cs.pdf

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

Drone-surveillance for search and rescue in natural disaster

Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)

Unmanned aerial vehicles (UAVs) play a primary role in a plethora of technical and scientific fields owing to their wide range of applications. In particular, the provision of emergency services during the occurrence of a crisis event is a vital application domain where such aerial robots can contribute, sending out valuable assistance to both distressed humans and rescue teams. Bearing in mind that time constraints constitute a crucial parameter in search and rescue (SAR) missions, the punctual and precise detection of humans in peril is of paramount importance. The paper in hand deals with real-time human detection onboard a fully autonomous rescue UAV. Using deep learning techniques, the implemented embedded system was capable of detecting open water swimmers. This allowed the UAV to provide assistance accurately in a fully unsupervised manner, thus enhancing first responder operational capabilities. The novelty of the proposed system is the combination of global navigation satellite system (GNSS) techniques and computer vision algorithms for both precise human detection and rescue apparatus release. Details about hardware configuration as well as the system’s performance evaluation are fully discussed.

Unsupervised Human Detection with an Embedded Vision System on a Fully Autonomous UAV for Search and Rescue Operations

Convolutional neural networks for object detection in aerial imagery for disaster response and recovery

There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network’s training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the “YOLO” (“You Only Look Once”) platform, objects can be tracked, detected (“seen”), and classified (“comprehended”) from video feeds supplied by UAVs in real-time.

/pdf/object-recognition-in-aerial-images-using-convolutional-4t442bid2k.pdf

Object Recognition in Aerial Images Using Convolutional Neural Networks

Pavement Crack Classification Based on Tensor Factorization

Massive infrastructure investment has dominated news headlines in the past few years. Infrastructure in the United States has become a major issue due to its current state which may be inadequate to support the economy in the next few years. With the new administration calling for increased private sector participation in infrastructure development, it will be beneficial to assess previous public-private sector partnership (PPP) projects which have not been entirely successful to avoid repeating past mistakes that can cost taxpayers and investors millions of dollars.

Lessons to be learned from bankruptcy filings in public-private partnership (PPP) projects in the United States

The National Bridge Inventory (NBI) has detailed information on over 600,000 bridges nationwide. The data, which spans a period of more than 20 years can be very useful for analyzing and modeling bridge performance. Previous analysis methods employ a 2-dimensional view of data which may result in the loss of subtle trends and changes in the data. Bridge data is inherently multidimensional and as such there is an added advantage in analyzing it in its natural multidimensional state. This paper focuses on the use of a multiway data analysis approach known as tensor decomposition to analyze the structural deficiency rate with respect to states, bridge structural types and time. The tensor decomposition approach is able to reveal clusters and patterns which are not easily perceptible when using conventional statistical tools.

Multiway Analysis of bridge structural types in the National Bridge Inventory (NBI): A tensor decomposition approach

The nation’s bridges are critical assets that facilitate travel and commerce, making them important components of economic growth. However, bridge repair and rehabilitation needs far exceed...

Offei Adarkwa

Papers

Object Recognition in Aerial Images Using Convolutional Neural Networks

Pavement Crack Classification Based on Tensor Factorization

Lessons to be learned from bankruptcy filings in public-private partnership (PPP) projects in the United States

Multiway Analysis of bridge structural types in the National Bridge Inventory (NBI): A tensor decomposition approach

Using Tensor Factorization to Predict Network-Level Performance of Bridges