Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy

The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges) were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1) was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects

Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201

Estimating Chicago's tree cover and canopy height using multi-spectral satellite imagery

Information on urban tree canopies is fundamental to mitigating climate change [1] as well as improving quality of life [2]. Urban tree planting initiatives face a lack of up-to-date data about the horizontal and vertical dimensions of the tree canopy in cities. We present a pipeline that utilizes LiDAR data as ground-truth and then trains a multi-task machine learning model to generate reliable estimates of tree cover and canopy height in urban areas using multi-source multi-spectral satellite imagery for the case study of Chicago.Comment: 4 pages, 4 figures, Submitted to Tackling Climate Change with Machine Learning: workshop at NeurIPS 202

Extracting Physical and Environmental Information of Irish Roads Using Airborne and Mobile Sensors

Airborne sensors including LiDAR and digital cameras are now used extensively for capturing topographical information as these are often more economical and efficient as compared to the traditional photogrammetric and land surveying techniques. Data captured using airborne sensors can be used to extract 3D information important for, inter alia, city modelling, land use classification and urban planning. According to the EU noise directive (2002/49/EC), the National Road Authority (NRA) in Ireland is responsible for generating noise models for all roads which are used by more than 8,000 vehicles per day. Accordingly, the NRA has to cover approximately 4,000 km of road, 500m on each side. These noise models have to be updated every 5 years. Important inputs to noise model are digital terrain model (DTM), 3D building data, road width, road centre line, ground surface type and noise barriers. The objective of this research was to extract these objects and topographical information using nationally available datasets acquired from the Ordnance Survey of Ireland (OSI). The OSI uses ALS50-II LiDAR and ADS40 digital sensors for capturing ground information. Both sensors rely on direct georeferencing, minimizing the need for ground control points. Before exploiting the complementary nature of both datasets for information extraction, their planimetric and vertical accuracies were evaluated using independent ground control points. A new method was also developed for registration in case of any mismatch. DSMs from LiDAR and aerial images were used to find common points to determine the parameters of 2D conformal transformation. The developed method was also evaluated by the EuroSDR in a project which involved a number of partners. These measures were taken to ensure that the inputs to the noise model were of acceptable accuracy as recommended in the report (Assessment of Exposure to Noise, 2006) by the European Working Group. A combination of image classification techniques was used to extract information by the fusion of LiDAR and aerial images. The developed method has two phases, viz. object classification and object reconstruction. Buildings and vegetation were classified based on Normalized Difference Vegetation Index (NDVI) and a normalized digital surface model (nDSM). Holes in building segments were filled by object-oriented multiresolution segmentation. Vegetation that remained amongst buildings was classified using cues obtained from LiDAR. The short comings there in were overcome by developing an additional classification cue using multiple returns. The building extents were extracted and assigned a single height value generated from LiDAR nDSM. The extracted height was verified against the ground truth data acquired using terrestrial survey techniques. Vegetation was further classified into three categories, viz. trees, hedges and tree clusters based on shape parameter (for hedges) and distance from neighbouring trees (for clusters). The ground was classified into three surface types i.e. roads and parking area, exposed surface and grass. This was done using LiDAR intensity, NDVI and nDSM. Mobile Laser Scanning (MLS) data was used to extract walls and purpose built noise barriers, since these objects were not extractable from the available airborne sensor data. Principal Component Analysis (PCA) was used to filter points belonging to such objects. A line was then fitted to these points using robust least square fitting. The developed object extraction method was tested objectively in two independent areas namely the Test Area-1 and the Test Area-2. The results were thoroughly investigated by three different accuracy assessment methods using the OSI vector data. The acceptance of any developed method for commercial applications requires completeness and correctness values of 85% and 70% respectively. Accuracy measures obtained using the developed method of object extraction recommend its applicability for noise modellin

Quantifying the urban forest environment using dense discrete return LiDAR and aerial color imagery for segmentation and object-level biomass assessment

The urban forest is becoming increasingly important in the contexts of urban green space and recreation, carbon sequestration and emission offsets, and socio-economic impacts. In addition to aesthetic value, these green spaces remove airborne pollutants, preserve natural resources, and mitigate adverse climate changes, among other benefits. A great deal of attention recently has been paid to urban forest management. However, the comprehensive monitoring of urban vegetation for carbon sequestration and storage is an under-explored research area. Such an assessment of carbon stores often requires information at the individual tree level, necessitating the proper masking of vegetation from the built environment, as well as delineation of individual tree crowns. As an alternative to expensive and time-consuming manual surveys, remote sensing can be used effectively in characterizing the urban vegetation and man-made objects. Many studies in this field have made use of aerial and multispectral/hyperspectral imagery over cities. The emergence of light detection and ranging (LiDAR) technology, however, has provided new impetus to the effort of extracting objects and characterizing their 3D attributes - LiDAR has been used successfully to model buildings and urban trees. However, challenges remain when using such structural information only, and researchers have investigated the use of fusion-based approaches that combine LiDAR and aerial imagery to extract objects, thereby allowing the complementary characteristics of the two modalities to be utilized. In this study, a fusion-based classification method was implemented between high spatial resolution aerial color (RGB) imagery and co-registered LiDAR point clouds to classify urban vegetation and buildings from other urban classes/cover types. Structural, as well as spectral features, were used in the classification method. These features included height, flatness, and the distribution of normal surface vectors from LiDAR data, along with a non-calibrated LiDAR-based vegetation index, derived from combining LiDAR intensity at 1064 nm with the red channel of the RGB imagery. This novel index was dubbed the LiDAR-infused difference vegetation index (LDVI). Classification results indicated good separation between buildings and vegetation, with an overall accuracy of 92% and a kappa statistic of 0.85. A multi-tiered delineation algorithm subsequently was developed to extract individual tree crowns from the identified tree clusters, followed by the application of species-independent biomass models based on LiDAR-derived tree attributes in regression analysis. These LiDAR-based biomass assessments were conducted for individual trees, as well as for clusters of trees, in cases where proper delineation of individual trees was impossible. The detection accuracy of the tree delineation algorithm was 70%. The LiDAR-derived biomass estimates were validated against allometry-based biomass estimates that were computed from field-measured tree data. It was found out that LiDAR-derived tree volume, area, and different distribution parameters of height (e.g., maximum height, mean of height) are important to model biomass. The best biomass model for the tree clusters and the individual trees showed an adjusted R-Squared value of 0.93 and 0.58, respectively. The results of this study showed that the developed fusion-based classification approach using LiDAR and aerial color (RGB) imagery is capable of producing good object detection accuracy. It was concluded that the LDVI can be used in vegetation detection and can act as a substitute for the normalized difference vegetation index (NDVI), when near-infrared multiband imagery is not available. Furthermore, the utility of LiDAR for characterizing the urban forest and associated biomass was proven. This work could have significant impact on the rapid and accurate assessment of urban green spaces and associated carbon monitoring and management

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

Building polygon extraction from aerial images and digital surface models with a frame field learning framework

Deep learning-based models for building delineation from remotely sensed images face the challenge of producing precise and regular building outlines. This study investigates the combination of normalized digital surface models (nDSMs) with aerial images to optimize the extraction of building polygons using the frame field learning method. Results are evaluated at pixel, object, and polygon levels. In addition, an analysis is performed to assess the statistical deviations in the number of vertices of building polygons compared with the reference. The comparison of the number of vertices focuses on finding the output polygons that are the easiest to edit by human analysts in operational applications. It can serve as guidance to reduce the post-processing workload for obtaining high-accuracy building footprints. Experiments conducted in Enschede, the Netherlands, demonstrate that by introducing nDSM, the method could reduce the number of false positives and prevent missing the real buildings on the ground. The positional accuracy and shape similarity was improved, resulting in better-aligned building polygons. The method achieved a mean intersection over union (IoU) of 0.80 with the fused data (RGB + nDSM) against an IoU of 0.57 with the baseline (using RGB only) in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures

A Review on Deep Learning in UAV Remote Sensing

Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms' applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicles (UAV) based applications have dominated aerial sensing research. However, a literature revision that combines both "deep learning" and "UAV remote sensing" thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published material and evaluated their characteristics regarding application, sensor, and technique used. We relate how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. Our revision consists of a friendly-approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.Comment: 38 pages, 10 figure

Monitoring canopy quality and improving equitable outcomes of urban tree planting using LiDAR and machine learning

Urban tree canopies are fundamental to mitigating the impacts of climate change within cities as well as providing a range of other important ecosystem, health, and amenity benefits. However, urban tree planting initiatives do not typically utilize data about both the horizontal and vertical dimensions of the tree canopy, despite height being a critical determinant of the quality and value of urban canopy cover. We present a novel pipeline that uses airborne LiDAR data to train a multi-task machine learning model to generate estimates of both canopy cover and height in urban areas. We apply this to multi-source multi-spectral imagery for the case study of Chicago, USA. Our results indicate that a multi-task UNet convolutional neural network can be used to generate reliable estimates of canopy cover and height from aerial and satellite imagery. We then use these canopy estimates to allocate 75,000 trees from Chicago's recent green initiative under four scenarios, minimizing the urban heat island effect and then optimizing for an equitable canopy distribution, comparing results when only canopy cover is used, and when both canopy cover and height are considered. Through the introduction of this novel pipeline, we show that including canopy height within decision-making processes allows the distribution of new trees to be optimised to further reduce the urban heat island effect in localities where trees have the highest cooling potential and allows trees to be more equitably distributed to communities with lower quality canopies

Real-time Aerial Vehicle Detection and Tracking using a Multi-modal Optical Sensor

Vehicle tracking from an aerial platform poses a number of unique challenges including the small number of pixels representing a vehicle, large camera motion, and parallax error. For these reasons, it is accepted to be a more challenging task than traditional object tracking and it is generally tackled through a number of different sensor modalities. Recently, the Wide Area Motion Imagery sensor platform has received reasonable attention as it can provide higher resolution single band imagery in addition to its large area coverage. However, still, richer sensory information is required to persistently track vehicles or more research on the application of WAMI for tracking is required. With the advancements in sensor technology, hyperspectral data acquisition at video frame rates become possible as it can be cruical in identifying objects even in low resolution scenes. For this reason, in this thesis, a multi-modal optical sensor concept is considered to improve tracking in adverse scenes. The Rochester Institute of Technology Multi-object Spectrometer is capable of collecting limited hyperspectral data at desired locations in addition to full-frame single band imagery. By acquiring hyperspectral data quickly, tracking can be achieved at reasonableframe rates which turns out to be crucial in tracking. On the other hand, the relatively high cost of hyperspectral data acquisition and transmission need to be taken into account to design a realistic tracking. By inserting extended data of the pixels of interest we can address or avoid the unique challenges posed by aerial tracking. In this direction, we integrate limited hyperspectral data to improve measurement-to-track association. Also, a hyperspectral data based target detection method is presented to avoid the parallax effect and reduce the clutter density. Finally, the proposed system is evaluated on realistic, synthetic scenarios generated by the Digital Image and Remote Sensing software