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1. An automated procedure to determine construction year of roads in forested landscapes using a least‐cost path and a Before‐After Control‐Impact approach

   https://doi.org/10.1002/rse2.376  

   Valle, Denis ; Rifai, Sami W. ; Carrero, Gabriel C. ; Meiga, Ana Y. Y. ( December 2023 , Remote Sensing in Ecology and Conservation)

   Proximity to roads is one of the main determinants of deforestation in the Amazon basin. Determining the construction year of roads (CYR) is critical to improve the understanding of the drivers of road construction and to enable predictions of the expansion of the road network and its consequent impact on ecosystems. While recent artificial intelligence approaches have been successfully used for road extraction, they have typically relied on high spatial‐resolution imagery, precluding their adoption for the determination of CYR for older roads. In this article, we developed a new approach to automate the process of determining CYR that relies on the approximate position of the current road network and a time‐series of the proportion of exposed soil based on the multidecadal remote sensing imagery from the Landsat program. Starting with these inputs, our methodology relies on the Least Cost Path algorithm to co‐register the road network and on a Before‐After Control‐Impact design to circumvent the inherent image‐to‐image variability in the estimated amount of exposed soil. We demonstrate this approach for a 357 000 km²area around the Transamazon highway (BR‐230) in the Brazilian Amazon, encompassing 36 240 road segments. The reliability of this approach is assessed by comparing the estimated CYR using our approach to the observed CYR based on a time‐series of Landsat images. This exercise reveals a close correspondence between the estimated and observed CYR (). Finally, we show how these data can be used to assess the effectiveness of protected areas (PAs) in reducing the yearly rate of road construction and thus their vulnerability to future degradation. In particular, we find that integral protection PAs in this region were generally more effective in reducing the expansion of the road network when compared to sustainable use PAs.

    

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2. A Chain Reaction of Infrastructural Development and Its Local Social Consequences: The Case of the Western Section of the BAM.

   https://doi.org/10.24412/1026-8804-2021-1-125-145  

   Kuklina, Vera V ; Krasnoshtanova, Natalia Ye ( January 2021 , Rossiâ i ATR)

   In this paper we propose to discuss the issues of a chain reaction caused by the development of different types of transport infrastructure. The issues of infrastructural projects impact on local communities have long been widely discussed by social researchers. However, the northern and Arctic regions remain less studied in this regard while the situation there is more complex, especially at the intersections of various types of transport infrastructure. The problems of interaction between different kinds of infrastructure are especially manifested in the regions of extractive development, such as the north of the Irkutsk Region and the Republic of Buryatia. The materials for the analysis were obtained as a result of field studies in the north of the Irkutsk Region and the Republic of Buryatia in 2016, 2017, 2019, and 2020, as well as on the basis of the analysis of satellite data and maps of the regional infrastructure development. The construction of the Baikal-Amur Mainline and later oil and gas pipelines became an impetus for the development of other types of infrastructure, not only in the oil and gas extractive activities but also in logging. However, limited access to the pipeline and oil service roads has caused conflicts between the industrial companies and the local population. Local hunting trails are at the lowest level in such a hierarchy and are often destroyed and blocked by infrastructure development. A deeper and more detailed analysis of the social consequences of infrastructural development contributes to an understanding of the hierarchies of power that exist in Russia. 

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3. Smart Traffic Management System using Deep Learning for Smart City Applications

   https://doi.org/10.1109/CCWC.2019.8666539  

   Lingani, Guy M. ; Rawat, Danda B. ; Garuba, Moses ( January 2019 , 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC))

   Already known as densely populated areas with land use including housing, transportation, sanitation, utilities and communication, nowadays, cities tend to grow even bigger. Genuine road-user's types are emerging with further technological developments to come. As cities population size escalates, and roads getting congested, government agencies such as Department of Transportation (DOT) through the National Highway Traffic Safety Administration (NHTSA) are in pressing need to perfect their management systems with new efficient technologies. The challenge is to anticipate on never before seen problems, in their effort to save lives and implement sustainable cost-effective management systems. To make things yet more complicated and a bit daunting, self-driving car will be authorized in a close future in crowded major cities where roads are to be shared among pedestrians, cyclists, cars, and trucks. Roads sizes and traffic signaling will need to be constantly adapted accordingly. Counting and classifying turning vehicles and pedestrians at an intersection is an exhausting task and despite traffic monitoring systems use, human interaction is heavily required for counting. Our approach to resolve traffic intersection turning-vehicles counting is less invasive, requires no road dig up or costly installation. Live or recorded videos from already installed camera all over the cities can be used as well as any camera including cellphones. Our system is based on Neural Network and Deep Learning of object detection along computer vision technology and several methods and algorithms. Our approach will work on still images, recorded-videos, real-time live videos and will detect, classify, track and compute moving object velocity and direction using convolution neural network. Created based upon series of algorithms modeled after the human brain, our system uses NVIDIA Video cards with GPU, CUDA, OPENCV and mathematical vectors systems to perform. 

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4. Comparing Directed and Weighted Road Maps

   https://doi.org/10.1007/978-3-319-89593-2_4  

   Bittner, Alyson ; Fasy, Brittany Terese ; Grudzien, Maia ; Hajra, Sayonita Ghosh ; Huang, Jici ; Pelatt, Kristine ; Thatcher, Courtney ; Tumurbaatar, Altansuren ; Wenk, Carola ( July 2018 , Association for Women in Mathematics series)

   With the increasing availability of GPS trajectory data, map construction algorithms have been developed that automatically construct road maps from this data. In order to assess the quality of such (constructed) road maps, the need for meaningful road map comparison algorithms becomes increasingly important. Indeed, different approaches for map comparison have been recently proposed; however, most of these approaches assume that the road maps are modeled as undirected embedded planar graphs. In this paper, we study map comparison algorithms for more realistic models of road maps: directed roads as well as weighted roads. In particular, we address two main questions: how close are the graphs to each other, and how close is the information presented by the graphs (i.e., traffic times, trajectories, and road type)? We propose new road network comparisons and give illustrative examples. Furthermore, our approaches do not only apply to road maps but can be used to compare other kinds of graphs as well. 

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5. Historical road network statistics for core-based statistical areas in the U.S. (1900 - 2010)

   https://doi.org/10.6084/m9.figshare.19584088.v1  

   Burghardt, Keith ; Uhl, Johannes H. ( January 2022 , figshare)

   
   

   Tabulated statistics of road networks at the level of intersections and for built-up areas for each decade from 1900 to 2010, and for 2015, for each core-based statistical area (CBSA, i.e., metropolitan and micropolitan statistical area) in the conterminous United States. These areas are derived from historical road networks developed by Johannes Uhl. See Burghardt et al. (2022) for details on the data processing. 

   
   

   Spatial coverage: all CBSAs that are covered by the HISDAC-US historical settlement layers.

   This dataset includes around 2,700 U.S. counties. In the remaining counties, construction year coverage in the underlying ZTRAX data (Zillow Transaction and Assessment Dataset) is low. See Uhl et al. (2021) for details.

   All data created by Keith A. Burghardt, USC Information Sciences Institute, USA

   
   

   Codebook: these CBSA statistics are stratified by degree of aggregation.

   - CBSA_stats_diffFrom1950: Change in CBSA-aggregated patch statistics between 1950 and 2015

   - CBSA_stats_by_decade: CBSA-aggregated patch statistics for each decade from 1900-2010 plus 2015

   - CBSA_stats_by_decade: CBSA-aggregated cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.

   - Patch_stats_by_decade: Individual patch statistics for each decade from 1900-2010 plus 2015

   - Patch_stats_by_decade: Individual cumulative patch statistics for each decade from 1900-2010 plus 2015. All roads created up to a given decade are used for calculating statistics.

   
   

   The statistics are the following:

   • msaid: CBSA code
   • id: (if patch statistics) arbitrary int unique to each patch within the CBSA that year
   • year: year of statistics
   • pop: population within all CBSA counties
   • patch_bupr: built up property records (BUPR) within a patch (or sum of patches within CBSA)
   • patch_bupl: built up property l (BUPL) within a patch (or sum of patches within CBSA)
   • patch_bua: built up area (BUA) within a patch (or sum of patches within CBSA)
   • all_bupr: Same as above but for all data in 2015 regardless of whether properties were in patches
   • all_bupl: Same as above but for all data in 2015 regardless of whether properties were in patches
   • all_bua: Same as above but for all data in 2015 regardless of whether properties were in patches
   • num_nodes: number of nodes (intersections)
   • num_edges: number of edges (roads between intersections)
   • distance: total road length in km
   • k_mean: mean number of undirected roads per intersection
   • k1: fraction of nodes with degree 1
   • k4plus: fraction of nodes with degree 4+
   • bearing: histogram of different bearings between intersections
   • entropy: entropy of bearing histogram
   • mean_local_gridness: Griddedness used in text
   • mean_local_gridness_max: Same as griddedness used in text but assumes we can have up to 3 quadrilaterals for degree 3 (maximum possible, although intersections will not necessarily create right angles)

   
   

   Code available at https://github.com/johannesuhl/USRoadNetworkEvolution.

   
   

   References:

   Burghardt, K., Uhl, J., Lerman, K.,  & Leyk, S. (2022). Road Network Evolution in the Urban and Rural  United States Since 1900. Computers, Environment and Urban Systems.

    

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