Automated Landslide-Risk Prediction Using Web GIS and Machine Learning Models

Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations—Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool

Remote sensing image captioning with pre-trained transformer models

Remote sensing images, and the unique properties that characterize them, are attracting increased attention from computer vision researchers, largely due to their many possible applications. The area of computer vision for remote sensing has effectively seen many recent advances, e.g. in tasks such as object detection or scene classification. Recent work in the area has also addressed the task of generating a natural language description of a given remote sensing image, effectively combining techniques from both natural language processing and computer vision. Despite some previously published results, there nonetheless are still many limitations and possibilities for improvement. It remains challenging to generate text that is fluid and linguistically rich while maintaining semantic consistency and good discrimination ability about the objects and visual patterns that should be described. The previous proposals that have come closest to achieving the goals of remote sensing image captioning have used neural encoder-decoder architectures, often including specialized attention mechanisms to help the system in integrating the most relevant visual features while generating the textual descriptions. Taking previous work into consideration, this work proposes a new approach for remote sensing image captioning, using an encoder-decoder model based on the Transformer architecture, and where both the encoder and the decoder are based on components from a pre-existing model that was already trained with large amounts of data. Experiments were carried out using the three main datasets that exist for assessing remote sensing image captioning methods, respectively the Sydney-captions, the \acrshort{UCM}-captions, and the \acrshort{RSICD} datasets. The results show improvements over some previous proposals, although particularly on the larger \acrshort{RSICD} dataset they are still far from the current state-of-art methods. A careful analysis of the results also points to some limitations in the current evaluation methodology, mostly based on automated n-gram overlap metrics such as BLEU or ROUGE

Unsupervised Automatic Detection Of Transient Phenomena In InSAR Time-Series using Machine Learning

The detection and measurement of transient episodes of crustal deformation from global InSAR datasets are crucial for a wide range of solid earth and natural hazard applications. But the large volumes of unlabelled data captured by satellites preclude manual systematic analysis, and the small signal-to-noise ratio makes the task difficult. In this thesis, I present a state-of-the-art, unsupervised and event-agnostic deep-learning based approach for the automatic identification of transient deformation events in noisy time-series of unwrapped InSAR images. I adopt an anomaly detection framework that learns the ‘normal’ spatio-temporal pattern of noise in the data, and which therefore identifies any transient deformation phenomena that deviate from this pattern as ‘anomalies’. The deep-learning model is built around a bespoke autoencoder that includes convolutional and LSTM layers, as well as a neural network which acts as a bridge between the encoder and decoder. I train our model on real InSAR data from northern Turkey and find it has an overall accuracy and true positive rate of around 85% when trying to detect synthetic deformation signals of length-scale > 350 m and magnitude > 4 cm. Furthermore, I also show the method can detect (1) a real Mw 5.7 earthquake in InSAR data from an entirely different region- SW Turkey, (2) a volcanic deformation in Domuyo, Argentina, (3) a synthetic slow-slip event and (4) an interseismic deformation around NAF in a descending frame in northern Turkey. Overall I show that my method is suitable for automated analysis of large, global InSAR datasets, and for robust detection and separation of deformation signals from nuisance signals in InSAR data

Deep Vision in Optical Imagery: From Perception to Reasoning

Deep learning has achieved extraordinary success in a wide range of tasks in computer vision field over the past years. Remote sensing data present different properties as compared to natural images/videos, due to their unique imaging technique, shooting angle, etc. For instance, hyperspectral images usually have hundreds of spectral bands, offering additional information, and the size of objects (e.g., vehicles) in remote sensing images is quite limited, which brings challenges for detection or segmentation tasks. This thesis focuses on two kinds of remote sensing data, namely hyper/multi-spectral and high-resolution images, and explores several methods to try to find answers to the following questions: - In comparison with natural images or videos in computer vision, the unique asset of hyper/multi-spectral data is their rich spectral information. But what this “additional” information brings for learning a network? And how do we take full advantage of these spectral bands? - Remote sensing images at high resolution have pretty different characteristics, bringing challenges for several tasks, for example, small object segmentation. Can we devise tailored networks for such tasks? - Deep networks have produced stunning results in a variety of perception tasks, e.g., image classification, object detection, and semantic segmentation. While the capacity to reason about relations over space is vital for intelligent species. Can a network/module with the capacity of reasoning benefit to parsing remote sensing data? To this end, a couple of networks are devised to figure out what a network learns from hyperspectral images and how to efficiently use spectral bands. In addition, a multi-task learning network is investigated for the instance segmentation of vehicles from aerial images and videos. Finally, relational reasoning modules are designed to improve semantic segmentation of aerial images

Difference Enhancement and Spatial-Spectral Non-Local Network for Change Detection in VHR Remote Sensing Images

Copyright © The Author(s) 2021. The popular Siamese convolutional neural networks (CNNs) for remote sensing (RS) image change detection (CD) often suffer from two problems. First, they either ignore the original information of bitemporal images or insufficiently utilize the difference information between bitemporal images, which leads to the low tightness of the changed objects. Second, Siamese CNNs always employ dual-branch encoders for CD, which increases computational cost. To address the above issues, this article proposes a network based on difference enhancement and spatial–spectral nonlocal (DESSN) for CD in very-high-resolution (VHR) images. This article makes threefold contributions. First, we design a difference enhancement (DE) module that can effectively learn the difference representation between foreground and background to reduce the impact of irrelevant changes on the detection results. Second, we present a spatial–spectral nonlocal (SSN) module that is different from vanilla nonlocal because multiscale spatial global features are incorporated to model the large-scale variation of objects during CD. The module can be used to strengthen the edge integrity and internal tightness of changed objects. Third, the asymmetric double convolution with Ghost (ADCG) module is exploited instead of standard convolution. The ADCG can not only refine the edge information of the changed objects, since horizontal and vertical convolutional kernels have good contour preservation advantages, but also greatly reduce the computational complexity of the proposed model. The experiments on two public VHR CD datasets demonstrate that the proposed network can provide higher detection accuracy and requires smaller memory usage than state-of-the-art networks.10.13039/501100017596-Natural Science Basic Research Program of Shaanxi (Grant Number: 2021JC-47); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61871259 and 61861024); 10.13039/501100000288-National Natural Science Foundation of China-Royal Society, U.K. (Grant Number: 61811530325 (IECnNSFCn170396)); Key Research and Development Program of Shaanxi (Grant Number: 2021ZDLGY08-07); Shaanxi Joint Laboratory of Artificial Intelligence (Grant Number: 2020SS-03); Special Construction Fund for Key Disciplines of Shaanxi Provincial Higher Education

Cyber Security and Critical Infrastructures

This book contains the manuscripts that were accepted for publication in the MDPI Special Topic "Cyber Security and Critical Infrastructure" after a rigorous peer-review process. Authors from academia, government and industry contributed their innovative solutions, consistent with the interdisciplinary nature of cybersecurity. The book contains 16 articles: an editorial explaining current challenges, innovative solutions, real-world experiences including critical infrastructure, 15 original papers that present state-of-the-art innovative solutions to attacks on critical systems, and a review of cloud, edge computing, and fog's security and privacy issues

Deep Learning for Aerial Scene Understanding in High Resolution Remote Sensing Imagery from the Lab to the Wild

Diese Arbeit präsentiert die Anwendung von Deep Learning beim Verständnis von Luftszenen, z. B. Luftszenenerkennung, Multi-Label-Objektklassifizierung und semantische Segmentierung. Abgesehen vom Training tiefer Netzwerke unter Laborbedingungen bietet diese Arbeit auch Lernstrategien für praktische Szenarien, z. B. werden Daten ohne Einschränkungen gesammelt oder Annotationen sind knapp

A Neural Network-Based Situational Awareness Approach for Emergency Response

publishedVersio

On the Design, Implementation and Application of Novel Multi-disciplinary Techniques for explaining Artificial Intelligence Models

284 p.Artificial Intelligence is a non-stopping field of research that has experienced some incredible growth lastdecades. Some of the reasons for this apparently exponential growth are the improvements incomputational power, sensing capabilities and data storage which results in a huge increment on dataavailability. However, this growth has been mostly led by a performance-based mindset that has pushedmodels towards a black-box nature. The performance prowess of these methods along with the risingdemand for their implementation has triggered the birth of a new research field. Explainable ArtificialIntelligence. As any new field, XAI falls short in cohesiveness. Added the consequences of dealing withconcepts that are not from natural sciences (explanations) the tumultuous scene is palpable. This thesiscontributes to the field from two different perspectives. A theoretical one and a practical one. The formeris based on a profound literature review that resulted in two main contributions: 1) the proposition of anew definition for Explainable Artificial Intelligence and 2) the creation of a new taxonomy for the field.The latter is composed of two XAI frameworks that accommodate in some of the raging gaps found field,namely: 1) XAI framework for Echo State Networks and 2) XAI framework for the generation ofcounterfactual. The first accounts for the gap concerning Randomized neural networks since they havenever been considered within the field of XAI. Unfortunately, choosing the right parameters to initializethese reservoirs falls a bit on the side of luck and past experience of the scientist and less on that of soundreasoning. The current approach for assessing whether a reservoir is suited for a particular task is toobserve if it yields accurate results, either by handcrafting the values of the reservoir parameters or byautomating their configuration via an external optimizer. All in all, this poses tough questions to addresswhen developing an ESN for a certain application, since knowing whether the created structure is optimalfor the problem at hand is not possible without actually training it. However, some of the main concernsfor not pursuing their application is related to the mistrust generated by their black-box" nature. Thesecond presents a new paradigm to treat counterfactual generation. Among the alternatives to reach auniversal understanding of model explanations, counterfactual examples is arguably the one that bestconforms to human understanding principles when faced with unknown phenomena. Indeed, discerningwhat would happen should the initial conditions differ in a plausible fashion is a mechanism oftenadopted by human when attempting at understanding any unknown. The search for counterfactualsproposed in this thesis is governed by three different objectives. Opposed to the classical approach inwhich counterfactuals are just generated following a minimum distance approach of some type, thisframework allows for an in-depth analysis of a target model by means of counterfactuals responding to:Adversarial Power, Plausibility and Change Intensity