Urban accessibility diagnosis from mobile laser scanning data

International audienceIn this paper we present an approach for automatic analysis of urban acessibility using 3D point clouds. Our approach is based on range images and it consists in two main steps: urban objects segmentation and curbs detection. Both of them are required for accessibility diagnosis and itinerary planning. Our method automatically segments facades and urban objects using two hypotheses: facades are the highest vertical structures in the scene and objects are bumps on the ground on the range image. The segmentation result is used to build an urban obstacle map. After that, the gradient is computed on the ground range image. Curb candidates are selected using height and geodesic features. Then, nearby curbs are reconnected using Bézier curves. Finally, accessibility is defined based on geometrical features and accessibility standards. Our methodology is tested on two MLS databases from Paris (France) and Enschede (The Netherlands). Our experiments show that our method has good detection rates, is fast and presents few false alarms. Our method outperforms other works reported in the literature on the same databases

Archaeological Prospecting Using Historic Aerial Imagery: Investigations in Northeast and Southwest Arkansas

This research investigates the potential of historic aerial photographs as a tool for archaeological site prospecting. Craighead and Mississippi Counties in northeast Arkansas and areas adjacent to the Red and Little Rivers in southwest Arkansas were chosen as study areas. These regions have undergone significant changes in the past few decades and were expected to yield visible types of archaeological sites. Historic aerial images of these areas were obtained through the U.S. Geological Survey\u27s EarthExplorer database (http://earthexplorer.usgs.gov/). These images were processed using Agisoft PhotoScan Professional to produce extensive regional orthoimages. Using the Arkansas Archeological Survey\u27s Automated Management of Archeological Site Data in Arkansas (AMASDA) database, known archaeological sites dating later than Late Woodland were compared against PhotoScan-generated orthoimagery to see if they were visible using a tripartite classification scheme: site invisible, site possibly visible, and site visible. Trends in site visibility were assessed in terms of the photographs\u27 characteristics (e.g., dates, geographic scales, download resolutions) and the nature of the archaeological sites (e.g., surface scatters, mound sites, middens, standing structures). For specific archaeological sites, possible archaeological, modern, and natural features were digitized. Within-site visibility was reexamined with respect to the sites\u27 temporal ranges, previously documented structures and features, seasonal differences of the imagery, and disturbances from modern land-use. Historic digital elevation models (DEMs) were generated in PhotoScan to assess the performance of the software\u27s geometry-building algorithm for intrasite prospecting. Overall, only a small percentage of specific site types (i.e., mounds, historic structures, middens) were classified as definitively visible. However, the site classification scheme used in this study provides a subset of sites with potential archaeological anomalies, which can be investigated more closely with site survey reports. High-resolution orthoimages and DEMs produced from stereopairs in PhotoScan also present archaeologically promising anomalies for subsequent analyses

Mapping horizontal and vertical urban densification in Denmark with Landsat time-series from 1985 to 2018: a semantic segmentation solution

Landsat imagery is an unparalleled freely available data source that allows reconstructing horizontal and vertical urban form. This paper addresses the challenge of using Landsat data, particularly its 30m spatial resolution, for monitoring three-dimensional urban densification. We compare temporal and spatial transferability of an adapted DeepLab model with a simple fully convolutional network (FCN) and a texture-based random forest (RF) model to map urban density in the two morphological dimensions: horizontal (compact, open, sparse) and vertical (high rise, low rise). We test whether a model trained on the 2014 data can be applied to 2006 and 1995 for Denmark, and examine whether we could use the model trained on the Danish data to accurately map other European cities. Our results show that an implementation of deep networks and the inclusion of multi-scale contextual information greatly improve the classification and the model's ability to generalize across space and time. DeepLab provides more accurate horizontal and vertical classifications than FCN when sufficient training data is available. By using DeepLab, the F1 score can be increased by 4 and 10 percentage points for detecting vertical urban growth compared to FCN and RF for Denmark. For mapping the other European cities with training data from Denmark, DeepLab also shows an advantage of 6 percentage points over RF for both the dimensions. The resulting maps across the years 1985 to 2018 reveal different patterns of urban growth between Copenhagen and Aarhus, the two largest cities in Denmark, illustrating that those cities have used various planning policies in addressing population growth and housing supply challenges. In summary, we propose a transferable deep learning approach for automated, long-term mapping of urban form from Landsat images.Comment: Accepted manuscript including appendix (supplementary file

Geographic Information Science (GIScience) and Geospatial Approaches for the Analysis of Historical Visual Sources and Cartographic Material

This book focuses on the use of GIScience in conjunction with historical visual sources to resolve past scenarios. The themes, knowledge gained and methodologies conducted might be of interest to a variety of scholars from the social science and humanities disciplines

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

NEW ADVANCED TECHNOLOGIES FOR SURVEY AND ANALYSISbOF AGROFORESTRY LAND: FROM LAND COVER CHANGES TO RURAL LANDSCAPE QUALITY ASSESSMENT

The general objective of this Ph.D. thesis is to explore the concepts and methodologies for investigating agroforestry land and rural landscape through the integration of historical and remote sensing geodata within a FoSS (Free and Open Source Software) approach; to provide more and more accurate data sets regarding land cover and to improve some mapping and data processing techniques commonly used in this research topic. The first part of thesis describes the different types of geodata used in the course of the studies and, above all, the techniques and methodologies used for their processing are illustrated. Starting from historical cartographies, we will go through aerial surveys and geographical maps up to the new remote sensing using advanced satellite observation technologies. In the second part, more specific issues were dealt in accordance with the general objective of the work have been defined. The issues were approached through case studies within the Basilicata Region where the intensity of the abandonment of the territory and agricultural surface is leading to the loss of many historical rural landscapes and with consequent problems from an ecological point of view due to the disappearance of many agroforestry systems.L'obiettivo generale di questa tesi di dottorato è quello di esplorare i concetti e le metodologie per lo studio del territorio agroforestale e del paesaggio rurale attraverso l'integrazione di geodati storici e telerilevamento con un approccio FoSS (Free and Open Source Software); per fornire serie di dati sempre più accurate sulla copertura del suolo e migliorare alcune tecniche di mappatura ed elaborazione comunemente utilizzate in questo ambito di ricerca. La prima parte della tesi descrive i diversi tipi di geodati impiegati nel corso degli studi e, soprattutto, vengono illustrate le tecniche e le metodologie utilizzate per la loro elaborazione. Partendo dalle cartografie storiche, si passerà ai rilievi aerei ed alle cartogrofaie classifche fino al remote sensing basato su immagini satellitari. Nella seconda parte sono state trattate tematiche più specifiche in accordo con l'obiettivo generale del lavoro. Le tematiche sono state affrontate attraverso casi di studio all'interno della Regione Basilicata dove l'intensità dell'abbandono del territorio e della superficie agricola sta portando alla perdita di molti paesaggi rurali storici con conseguenti problemi dal punto di vista ecologico dovuti alla scomparsa di molti sistemi agroforestali