Segmenting Roads from Aerial Images: A Deep Learning Approach Using Multi-Scale Analysis

Road map generation requires frequent map updates due to the irregular infrastructural changes. Updating a manual road map is a lengthy process, whereas using aerial or remote sensing (RS) requires less time for the update. However, road extraction becomes more complex due to the similar texture appearance of building top roofs, shadows, and occlusion due to trees. The occluded roads appear as discontinuous road patch in segmented image of updated maps. In this paper, we propose a deep learning method that uses multi-scale analysis for road feature extraction. The dilated inception module (DI) in the up and down sampling paths of network extracts the local and global texture patterns of the road. Furthermore, we also utilize the pyramid pooling module (PP) which has average and max pooling to study the global contextual information under the shadow regions. In the proposed architecture, first, the road in the aerial images is segmented along with the tiny non-road segments. Next, the post processing, which exploits the geometrical shape features, is utilized for filtering the tiny non-road noises. The performance of proposed network is validated on using the publicly available Massachusetts road data by comparing with the other models available in literature

Automatic Rural Road Centerline Extraction from Aerial Images for a Forest Fire Support System

In the last decades, Portugal has been severely affected by forest fires which have caused massive damage both environmentally and socially. Having a well-structured and precise mapping of rural roads is critical to help firefighters to mitigate these events. The traditional process of extracting rural roads centerlines from aerial images is extremely time-consuming and tedious, because the mapping operator has to manually label the road area and extract the road centerline. A frequent challenge in the process of extracting rural roads centerlines is the high amount of environmental complexity and road occlusions caused by vehicles, shadows, wild vegetation, and trees, bringing heterogeneous segments that can be further improved. This dissertation proposes an approach to automatically detect rural road segments as well as extracting the road centerlines from aerial images. The proposed method focuses on two main steps: on the first step, an architecture based on a deep learning model (DeepLabV3+) is used, to extract the road features maps and detect the rural roads. On the second step, the first stage of the process is an optimization for improving road connections, as well as cleaning white small objects from the predicted image by the neural network. Finally, a morphological approach is proposed to extract the rural road centerlines from the previously detected roads by using thinning algorithms like the Zhang-Suen and Guo-Hall methods. With the automation of these two stages, it is now possible to detect and extract road centerlines from complex rural environments automatically and faster than the traditional ways, and possibly integrating that data in a Geographical Information System (GIS), allowing the creation of real-time mapping applications.Nas últimas décadas, Portugal tem sido severamente afetado por fogos florestais, que têm causado grandes estragos ambientais e sociais. Possuir um sistema de mapeamento de estradas rurais bem estruturado e preciso é essencial para ajudar os bombeiros a mitigar este tipo de eventos. Os processos tradicionais de extração de eixos de via em estradas rurais a partir de imagens aéreas são extremamente demorados e fastidiosos. Um desafio frequente na extração de eixos de via de estradas rurais é a alta complexidade dos ambientes rurais e de estes serem obstruídos por veículos, sombras, vegetação selvagem e árvores, trazendo segmentos heterogéneos que podem ser melhorados. Esta dissertação propõe uma abordagem para detetar automaticamente estradas rurais, bem como extrair os eixos de via de imagens aéreas. O método proposto concentra-se em duas etapas principais: na primeira etapa é utilizada uma arquitetura baseada em modelos de aprendizagem profunda (DeepLabV3+), para detetar as estradas rurais. Na segunda etapa, primeiramente é proposta uma otimização de intercessões melhorando as conexões relativas aos eixos de via, bem como a remoção de pequenos artefactos que estejam a introduzir ruído nas imagens previstas pela rede neuronal. E, por último, é utilizada uma abordagem morfológica para extrair os eixos de via das estradas previamente detetadas recorrendo a algoritmos de esqueletização tais como os algoritmos Zhang-Suen e Guo-Hall. Automatizando estas etapas, é então possível extrair eixos de via de ambientes rurais de grande complexidade de forma automática e com uma maior rapidez em relação aos métodos tradicionais, permitindo, eventualmente, integrar os dados num Sistema de Informação Geográfica (SIG), possibilitando a criação de aplicativos de mapeamento em tempo real

Object Detection in 20 Years: A Survey

Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years. Its development in the past two decades can be regarded as an epitome of computer vision history. If we think of today's object detection as a technical aesthetics under the power of deep learning, then turning back the clock 20 years we would witness the wisdom of cold weapon era. This paper extensively reviews 400+ papers of object detection in the light of its technical evolution, spanning over a quarter-century's time (from the 1990s to 2019). A number of topics have been covered in this paper, including the milestone detectors in history, detection datasets, metrics, fundamental building blocks of the detection system, speed up techniques, and the recent state of the art detection methods. This paper also reviews some important detection applications, such as pedestrian detection, face detection, text detection, etc, and makes an in-deep analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible publicatio

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure

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

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms