FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

In this paper, we develop deep spatio-temporal neural networks to sequentially count vehicles from low quality videos captured by city cameras (citycams). Citycam videos have low resolution, low frame rate, high occlusion and large perspective, making most existing methods lose their efficacy. To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion. Such design leverages the strengths of FCN for pixel-level prediction and the strengths of LSTM for learning complex temporal dynamics. The residual learning connection reformulates the vehicle count regression as learning residual functions with reference to the sum of densities in each frame, which significantly accelerates the training of networks. To preserve feature map resolution, we propose a Hyper-Atrous combination to integrate atrous convolution in FCN and combine feature maps of different convolution layers. FCN-rLSTM enables refined feature representation and a novel end-to-end trainable mapping from pixels to vehicle count. We extensively evaluated the proposed method on different counting tasks with three datasets, with experimental results demonstrating their effectiveness and robustness. In particular, FCN-rLSTM reduces the mean absolute error (MAE) from 5.31 to 4.21 on TRANCOS, and reduces the MAE from 2.74 to 1.53 on WebCamT. Training process is accelerated by 5 times on average.Comment: Accepted by International Conference on Computer Vision (ICCV), 201

Integrating Technologies for Scalable Ecology and Conservation

Integration of multiple technologies greatly increases the spatial and temporal scales over which ecological patterns and processes can be studied, and threats to protected ecosystems can be identified and mitigated. A range of technology options relevant to ecologists and conservation practitioners are described, including ways they can be linked to increase the dimensionality of data collection efforts. Remote sensing, ground-based, and data fusion technologies are broadly discussed in the context of ecological research and conservation efforts. Examples of technology integration across all of these domains are provided for large-scale protected area management and investigation of ecological dynamics. Most technologies are low-cost or open-source, and when deployed can reach economies of scale that reduce per-area costs dramatically. The large-scale, long-term data collection efforts presented here can generate new spatio-temporal understanding of threats faced by natural ecosystems and endangered species, leading to more effective conservation strategies

Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur

Commercial Satellite Imagery as an Evolving Open-Source Verification Technology: Emerging Trends and Their Impact for Nuclear Nonproliferation Analysis

One evolving and increasingly important means of verification of a State’s compliance with its international security obligations involves the application of publicly available commercial satellite imagery. The International Atomic Energy Agency (IAEA) views commercial satellite imagery as “a particularly valuable open source of information.” In 2001, the IAEA established an in-house Satellite Imagery Analysis Unit (SIAU) to provide an independent capability for "the exploitation of satellite imagery which involves imagery analysis, including correlation/fusion with other sources (open source, geospatial, and third party). Commercial satellite imagery not only supports onsite inspection planning and verification of declared activities,” but perhaps its most important role is that it also “increases the possibility of detecting proscribed nuclear activities.” Analysis of imagery derived from low-earth-orbiting observation satellites has a long history dating to the early 1906s in the midst of the Cold War era. That experience provides a sound basis for effectively exploiting the flood of now publicly available commercial satellite imagery data that is now within reach of anyone with Internet access. This paper provides insights on the process of imagery analysis, together with the use of modern geospatial tools like Google Earth, and highlights a few of the potential pitfalls that can lead to erroneous analytical conclusions. A number of illustrative exemplar cases are reviewed to illustrate how academic researchers (including those within the European Union’s Joint Research Centre) and others in Non-Governmental Organizations are now applying commercial satellite imagery in combination with other open source information in innovative and effective ways for various verification purposes. The international constellation of civil imaging satellites is rapidly growing larger, thereby improving the temporal resolution (reducing the time between image acquisitions), but the satellites are also significantly improving in capabilities with regard to both spatial and spectral resolutions. The significant increase, in both the volume and type of raw imagery data that these satellites can provide, and the ease of access to it, will likely lead to a concomitant increase in new non-proliferation relevant knowledge as well. Many of these new developments were previously unanticipated, and they have already had profound effects beyond what anyone would have thought possible just a few years ago. Among those include multi-satellite, multi-sensor synergies deriving from the diversity of sensors and satellites now available, which are exemplified in a few case studies. This paper also updates earlier work on the subject by this author and explains how the many recent significant developments in the commercial satellite imaging domain will play an ever increasingly valuable role for open source nuclear nonproliferation monitoring and verification in the future.JRC.E.8-Nuclear securit

Spatiotemporal analysis of traffic crashes involving pedestrians and cyclists in Jefferson County, Kentucky.

Walking and cycling are health-conscious, environmentally friendly modes of transportation, yet very few American trips are accomplished using these methods. A major factor behind this is the fear of being involved in a crash with an automobile. From 2009-2019 there were over 5,200 automobile crashes involving either pedestrians or cyclists in Louisville/ Jefferson County, Kentucky. Researchers have found that these kinds of crashes exhibit spatiotemporal patterns in different cities across the globe. The objective of this study was to determine if there exist any spatial and/or temporal patterns regarding these kinds of crashes. Data for this study came from the Kentucky State Police and encompassed all pedestrian and cyclist crashes from 2009-2019. GISsystems were used to perform a network-based kernel density estimation for the spatial analysis. For the temporal analysis, the scales of time, day and month were observed and plotted. Hot-spots were found to exist within the study area, with some locations being hot-spots for both pedestrian and cyclist crashes. These shared hot-spot locations were analyzed in detail, using the original Kentucky State Police data, as well as Google Earth and Street View imagery

Remote Sensing and Topographic Information in a GIS environment for Urban Growth and Change: Case Study Amman the Capital of Jordan

Urbanization results in the expansion of administrative boundaries, mainly at the periphery, ultimately leading to changes in landcover. Agricultural land, naturally vegetated land, and other land types are converted into residential areas with a high density of constructs, such as transportation systems and housing. In urban regions of rapid growth and change, urban planners need regular information on up to date ground change. Amman (the capital of Jordan) is growing at unprecedented rates, creating extensive urban landscapes. Planners interact with these changes without having a global view of their impact. The use of aerial photographs and satellite images data combined with topographic information and field survey could provide effective information to develop urban change and growth inventory which could be explored towards producing a very important signature for the built-up area changes

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

A Comprehensive Review on Computer Vision Analysis of Aerial Data

With the emergence of new technologies in the field of airborne platforms and imaging sensors, aerial data analysis is becoming very popular, capitalizing on its advantages over land data. This paper presents a comprehensive review of the computer vision tasks within the domain of aerial data analysis. While addressing fundamental aspects such as object detection and tracking, the primary focus is on pivotal tasks like change detection, object segmentation, and scene-level analysis. The paper provides the comparison of various hyper parameters employed across diverse architectures and tasks. A substantial section is dedicated to an in-depth discussion on libraries, their categorization, and their relevance to different domain expertise. The paper encompasses aerial datasets, the architectural nuances adopted, and the evaluation metrics associated with all the tasks in aerial data analysis. Applications of computer vision tasks in aerial data across different domains are explored, with case studies providing further insights. The paper thoroughly examines the challenges inherent in aerial data analysis, offering practical solutions. Additionally, unresolved issues of significance are identified, paving the way for future research directions in the field of aerial data analysis.Comment: 112 page