Review on Active and Passive Remote Sensing Techniques for Road Extraction

Digital maps of road networks are a vital part of digital cities and intelligent transportation. In this paper, we provide a comprehensive review on road extraction based on various remote sensing data sources, including high-resolution images, hyperspectral images, synthetic aperture radar images, and light detection and ranging. This review is divided into three parts. Part 1 provides an overview of the existing data acquisition techniques for road extraction, including data acquisition methods, typical sensors, application status, and prospects. Part 2 underlines the main road extraction methods based on four data sources. In this section, road extraction methods based on different data sources are described and analysed in detail. Part 3 presents the combined application of multisource data for road extraction. Evidently, different data acquisition techniques have unique advantages, and the combination of multiple sources can improve the accuracy of road extraction. The main aim of this review is to provide a comprehensive reference for research on existing road extraction technologies.Peer reviewe

Reducing the Burden of Aerial Image Labelling Through Human-in-the-Loop Machine Learning Methods

This dissertation presents an introduction to human-in-the-loop deep learning methods for remote sensing applications. It is motivated by the need to decrease the time spent by volunteers on semantic segmentation of remote sensing imagery. We look at two human-in-the-loop approaches of speeding up the labelling of the remote sensing data: interactive segmentation and active learning. We develop these methods specifically in response to the needs of the disaster relief organisations who require accurately labelled maps of disaster-stricken regions quickly, in order to respond to the needs of the affected communities. To begin, we survey the current approaches used within the field. We analyse the shortcomings of these models which include outputs ill-suited for uploading to mapping databases, and an inability to label new regions well, when the new regions differ from the regions trained on. The methods developed then look at addressing these shortcomings. We first develop an interactive segmentation algorithm. Interactive segmentation aims to segment objects with a supervisory signal from a user to assist the model. Work within interactive segmentation has focused largely on segmenting one or few objects within an image. We make a few adaptions to allow an existing method to scale to remote sensing applications where there are tens of objects within a single image that needs to be segmented. We show a quantitative improvements of up to 18% in mean intersection over union, as well as qualitative improvements. The algorithm works well when labelling new regions, and the qualitative improvements show outputs more suitable for uploading to mapping databases. We then investigate active learning in the context of remote sensing. Active learning looks at reducing the number of labelled samples required by a model to achieve an acceptable performance level. Within the context of deep learning, the utility of the various active learning strategies developed is uncertain, with conflicting results within the literature. We evaluate and compare a variety of sample acquisition strategies on the semantic segmentation tasks in scenarios relevant to disaster relief mapping. Our results show that all active learning strategies evaluated provide minimal performance increases over a simple random sample acquisition strategy. However, we present analysis of the results illustrating how the various strategies work and intuition of when certain active learning strategies might be preferred. This analysis could be used to inform future research. We conclude by providing examples of the synergies of these two approaches, and indicate how this work, on reducing the burden of aerial image labelling for the disaster relief mapping community, can be further extended

Efficient Multi-Objective NeuroEvolution in Computer Vision and Applications for Threat Identification

Concealed threat detection is at the heart of critical security systems designed to en- sure public safety. Currently, methods for threat identification and detection are primarily manual, but there is a recent vision to automate the process. Problematically, developing computer vision models capable of operating in a wide range of settings, such as the ones arising in threat detection, is a challenging task involving multiple (and often conflicting) objectives. Automated machine learning (AutoML) is a flourishing field which endeavours to dis- cover and optimise models and hyperparameters autonomously, providing an alternative to classic, effort-intensive hyperparameter search. However, existing approaches typ- ically show significant downsides, like their (1) high computational cost/greediness in resources, (2) limited (or absent) scalability to custom datasets, (3) inability to provide competitive alternatives to expert-designed and heuristic approaches and (4) common consideration of a single objective. Moreover, most existing studies focus on standard classification tasks and thus cannot address a plethora of problems in threat detection and, more broadly, in a wide variety of compelling computer vision scenarios. This thesis leverages state-of-the-art convolutional autoencoders and semantic seg- mentation (Chapter 2) to develop effective multi-objective AutoML strategies for neural architecture search. These strategies are designed for threat detection and provide in- sights into some quintessential computer vision problems. To this end, the thesis first introduces two new models, a practical Multi-Objective Neuroevolutionary approach for Convolutional Autoencoders (MONCAE, Chapter 3) and a Resource-Aware model for Multi-Objective Semantic Segmentation (RAMOSS, Chapter 4). Interestingly, these ap- proaches reached state-of-the-art results using a fraction of computational resources re- quired by competing systems (0.33 GPU days compared to 3150), yet allowing for mul- tiple objectives (e.g., performance and number of parameters) to be simultaneously op- timised. This drastic speed-up was possible through the coalescence of neuroevolution algorithms with a new heuristic technique termed Progressive Stratified Sampling. The presented methods are evaluated on a range of benchmark datasets and then applied to several threat detection problems, outperforming previous attempts in balancing multiple objectives. The final chapter of the thesis focuses on thread detection, exploiting these two mod- els and novel components. It presents first a new modification of specialised proxy scores to be embedded in RAMOSS, enabling us to further accelerate the AutoML process even more drastically while maintaining avant-garde performance (above 85% precision for SIXray). This approach rendered a new automatic evolutionary Multi-objEctive method for cOncealed Weapon detection (MEOW), which outperforms state-of-the-art models for threat detection in key datasets: a gold standard benchmark (SixRay) and a security- critical, proprietary dataset. Finally, the thesis shifts the focus from neural architecture search to identifying the most representative data samples. Specifically, the Multi-objectIve Core-set Discovery through evolutionAry algorithMs in computEr vision approach (MIRA-ME) showcases how the new neural architecture search techniques developed in previous chapters can be adapted to operate on data space. MIRA-ME offers supervised and unsupervised ways to select maximally informative, compact sets of images via dataset compression. This operation can offset the computational cost further (above 90% compression), with a minimal sacrifice in performance (less than 5% for MNIST and less than 13% for SIXray). Overall, this thesis proposes novel model- and data-centred approaches towards a more widespread use of AutoML as an optimal tool for architecture and coreset discov- ery. With the presented and future developments, the work suggests that AutoML can effectively operate in real-time and performance-critical settings such as in threat de- tection, even fostering interpretability by uncovering more parsimonious optimal models. More widely, these approaches have the potential to provide effective solutions to chal- lenging computer vision problems that nowadays are typically considered unfeasible for AutoML settings