A phase field model incorporating generic and specific prior knowledge applied to road network extraction from VHR satellite images.

We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach

A Phase Field Model Incorporating Generic and Specific Prior Knowledge Applied to Road Network Extraction from VHR Satellite Images

We address the problem of updating road maps in dense urban areas by extracting the main road network from a very high resolution (VHR) satellite image. Our model of the region occupied by the road network in the image is innovative. It incorporates three different types of prior geometric knowledge: generic boundary smoothness constraints, equivalent to a standard active contour prior; knowledge of the geometric properties of road networks (i.e. that they occupy regions composed of long, low-curvature segments joined at junctions), equivalent to a higher-order active contour prior; and knowledge of the road network at an earlier date derived from GIS data, similar to other ‘shape priors’ in the literature. In addition, we represent the road network region as a ‘phase field’, which offers a number of important advantages over other region modelling frameworks. All three types of prior knowledge prove important for overcoming the complexity of geometric ‘noise’ in VHR images. Promising results and a comparison with several other techniques demonstrate the effectiveness of our approach

Extraction of main and secondary roads in VHR images using a higher-order phase field model.

This paper addresses the issue of extracting main and secondary road networks in dense urban areas from very high resolution (VHR, ~0.61m) satellite images. The difficulty with secondary roads lies in the low discriminative power of the grey-level distributions of road regions and the background, and the greater effect of occlusions and other noise on narrower roads. To tackle this problem, we use a previously developed higher-order active contour (HOAC) phase field model and augment it with an additional non-linear non-local term. The additional term allows separate control of road width and road curvature; thus more precise prior knowledge can be incorporated, and better road prolongation can be achieved for the same width. Promising results on QuickBird panchromatic images at reduced resolutions and comparisons with other models demonstrate the role and the efficiency of our new model

Road Feature Extraction from High Resolution Aerial Images Upon Rural Regions Based on Multi-Resolution Image Analysis and Gabor Filters

Accurate, detailed and up-to-date road information is of special importance in geo-spatial databases as it is used in a variety of applications such as vehicle navigation, traffic management and advanced driver assistance systems (ADAS). The commercial road maps utilized for road navigation or the geographical information system (GIS) today are based on linear road centrelines represented in vector format with poly-lines (i.e., series of nodes and shape points, connected by segments), which present a serious lack of accuracy, contents, and completeness for their applicability at the sub-road level. For instance, the accuracy level of the present standard maps is around 5 to 20 meters. The roads/streets in the digital maps are represented as line segments rendered using different colours and widths. However, the widths of line segments do not necessarily represent the actual road widths accurately. Another problem with the existing road maps is that few precise sub-road details, such as lane markings and stop lines, are included, whereas such sub-road information is crucial for applications such as lane departure warning or lane-based vehicle navigation. Furthermore, the vast majority of roadmaps aremodelled in 2D space, whichmeans that some complex road scenes, such as overpasses and multi-level road systems, cannot be effectively represented. In addition, the lack of elevation information makes it infeasible to carry out applications such as driving simulation and 3D vehicle navigation

Combining Multiple Algorithms for Road Network Tracking from Multiple Source Remotely Sensed Imagery: a Practical System and Performance Evaluation

In light of the increasing availability of commercial high-resolution imaging sensors, automatic interpretation tools are needed to extract road features. Currently, many approaches for road extraction are available, but it is acknowledged that there is no single method that would be successful in extracting all types of roads from any remotely sensed imagery. In this paper, a novel classification of roads is proposed, based on both the roads' geometrical, radiometric properties and the characteristics of the sensors. Subsequently, a general road tracking framework is proposed, and one or more suitable road trackers are designed or combined for each type of roads. Extensive experiments are performed to extract roads from aerial/satellite imagery, and the results show that a combination strategy can automatically extract more than 60% of the total roads from very high resolution imagery such as QuickBird and DMC images, with a time-saving of approximately 20%, and acceptable spatial accuracy. It is proven that a combination of multiple algorithms is more reliable, more efficient and more robust for extracting road networks from multiple-source remotely sensed imagery than the individual algorithms

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

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth in Kampala. Three building feature characteristics of size, shape and Distance to nearest Neighbour were derived and used to cluster and classify informal areas using Hotspot Cluster analysis and ML approach on OSM buildings data. The resultant informal regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal changes in informal areas using Convolutional Neural Networks (CNNs). Results from Optimized Hot Spot Analysis and Random Forest Classification show that Informal regions can be mapped based on building outline characteristics. An accuracy of 90.3% was achieved when an optimally trained CNN was executed on a test set of 2019 satellite image tiles. Predictions of informality from new datasets for the years 2016 and 2017 provided promising results on combining different open source geospatial datasets to identify, classify and monitor informal settlements