Multi-dimensional modelling for the national mapping agency: a discussion of initial ideas, considerations, and challenges

The Ordnance Survey, the National Mapping Agency (NMA) for Great Britain, has recently begun to research the possible extension of its 2-dimensional geographic information into a multi-dimensional environment. Such a move creates a number of data creation and storage issues which the NMA must consider. Many of these issues are highly relevant to all NMA’s and their customers alike, and are presented and explored here. This paper offers a discussion of initial considerations which NMA’s face in the creation of multi-dimensional datasets. Such issues include assessing which objects should be mapped in 3 dimensions by a National Mapping Agency, what should be sensibly represented dynamically, and whether resolution of multi-dimensional models should change over space. This paper also offers some preliminary suggestions for the optimal creation method for any future enhanced national height model for the Ordnance Survey. This discussion includes examples of problem areas and issues in both the extraction of 3-D data and in the topological reconstruction of such. 3-D feature extraction is not a new problem. However, the degree of automation which may be achieved and the suitability of current techniques for NMA’s remains a largely unchartered research area, which this research aims to tackle. The issues presented in this paper require immediate research, and if solved adequately would mark a cartographic paradigm shift in the communication of geographic information – and could signify the beginning of the way in which NMA’s both present and interact with their customers in the future

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

Socioeconomic Factors Associated with Increasing Street Tree Density and Diversity in Central Indianapolis

Urban trees provide an abundance of benefits to city residents. Information about the geographic distribution of urban trees is critical to ensure equitable access to these benefits. Street trees are unique among urban trees because they are often managed by municipalities as a public resource, but they are challenging to manage in part because they are spatially dispersed across a city in close proximity to private property. While street tree inventory data sets are costly to generate, they provide important information to understand the spatial distribution of trees in the city and to plan for ongoing street tree management. Here, we utilize two street inventories collected at a 13-year interval to assess patterns and changes in municipal street tree distributions in Center Township, Indianapolis, IN. In the township as a whole, tree density, basal area, and taxonomic diversity increased markedly over time. Spatial autoregressive models were constructed to examine the relationship between socioeconomic characteristics and street tree population dynamics. Concurrent, legacy, and change models revealed significant associations between socioeconomic factors and street tree distributions. Specifically, tree density and taxonomic diversity were positively associated with educational attainment, and increases in tree density over time were negatively associated with the percent of the population that identifies as black or African American. Results suggest that despite overall increases in street trees, persistent inequalities in street tree access should be addressed in central Indianapolis neighborhoods

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

The status of environmental satellites and availability of their data products

The latest available information about the status of unclassified environmental satellite (flown by the United States) and their data products is presented. The type of environmental satellites discussed include unmanned earth resource and meteorological satellites, and manned satellites which can act as a combination platform for instruments. The capabilities and data products of projected satellites are discussed along with those of currently operating systems

C-blox: A Scalable and Consistent TSDF-based Dense Mapping Approach

In many applications, maintaining a consistent dense map of the environment is key to enabling robotic platforms to perform higher level decision making. Several works have addressed the challenge of creating precise dense 3D maps from visual sensors providing depth information. However, during operation over longer missions, reconstructions can easily become inconsistent due to accumulated camera tracking error and delayed loop closure. Without explicitly addressing the problem of map consistency, recovery from such distortions tends to be difficult. We present a novel system for dense 3D mapping which addresses the challenge of building consistent maps while dealing with scalability. Central to our approach is the representation of the environment as a collection of overlapping TSDF subvolumes. These subvolumes are localized through feature-based camera tracking and bundle adjustment. Our main contribution is a pipeline for identifying stable regions in the map, and to fuse the contributing subvolumes. This approach allows us to reduce map growth while still maintaining consistency. We demonstrate the proposed system on a publicly available dataset and simulation engine, and demonstrate the efficacy of the proposed approach for building consistent and scalable maps. Finally we demonstrate our approach running in real-time on-board a lightweight MAV.Comment: 8 pages, 5 figures, conferenc