Mapping the complexity of Transform Margins

Transform margins are a function of the pre-existing crustal architecture (pre-transform) and the interplay of syn- and post-transform geodynamic processes. We use a suite of geospatial databases to investigate four transform margins: East Africa (Davie Deformational Zone, DDZ), Equatorial Africa, and the South African and Falkland (Malvinas) margins (Agulhas-Falkland Fracture Zone, AFFZ). The East African margin is the most complex of the four. This is a consequence of Late Jurassic - Early Cretaceous transform motion affecting highly heterogeneous crust, and post-transform deformation that varies along the margin. Equatorial Africa most closely adheres to traditional definitions of “transform margins”, but actually comprises two principal transform systems - the Romanche and St. Pauls, dictated by the pre-transform distribution of mobile belts and West African craton. All four margins are spatially associated with volcanism, and each exhibits narrow uplifts associated with transpression or transtension. But the causal relationship of these features with transform processes differ. Volcanism along the East African margin is pre- and post-transform. Syn-transform volcanism on the AFFZ is spatially limited, with the AFFZ possibly acting as a conduit for magmatism rather than as a causal driver. Transform margins are varied and complex and require an understanding of pre-, syn- and post-transform geodynamics.

Remote sensing reveals multi-decadal losses of tree cover in California driven by increasing fire disturbance and climate stress

Forests provide natural climate solutions for sequestering carbon and mitigating climate change yet are threatened by increasing temperatures and disturbance. Accurate information on vegetation dynamics is lacking in some regions with forest carbon offset programs and dense forests like California. To address this, we combined remote sensing observations with geospatial databases to develop annual maps of vegetation cover (tree, shrub, herbaceous) and disturbance type (fires, harvest, and forest die-off) in California at 30 m resolution from 1985 to 2021. California lost 3783 km2 of its tree cover area (5.5% relative to initial cover). Early gains in tree cover area were more than offset by fire-driven declines, resulting in greater shrub and herbaceous cover area. Fires and tree cover area loss occurred where temperatures were high or increasing, whereas tree cover gain occurred in cooler areas. Disturbance and warming are threatening the integrity of California's forests and its carbon offsets program.

POSSIBILITIES OF OBTAINING CADASTRAL PLANS ON LARGE SURFACES USING AERIAL PHOTOGRAMMETRY

The importance of research in the field of topo-cadastral or photogrammetry has been revealed by many studies but strictly with reference to the general cadastre without considering the relevance for agriculture: arable land, pastoral space, forests or other uses. In this context, the purpose of the research was to bring "automated" alternatives, remotely, to the preparation, updating or completion of cadastral plans available in the past in analog format, at the level of each ATU. The working methodology consisted of: flying over the territory with WingtraOne photogrammetric equipment, at an altitude of 300 m, generating the orthophotoplan with very high spatial resolution (below 0.5 m), vectoring the lands according to the category of use, both in the urban area and outside the commune and the creation of geospatial databases. The obtained results materialized through the topo-cadastral inventory of all the buildings in the analyzed territory (arable land, pastures, hayfields, forests, built spaces, etc.), both as a spatial location (vector format) and as a descriptive database. The application of high precision photogrammetric techniques has a practical application in real estate inventory, especially in the case of large areas, but also a complement to theoretical research in various fields.

Benchmarking geospatial database on Kubernetes cluster

Kubernetes is an open-source container orchestration system for automating container application operations and has been considered to deploy various kinds of container workloads. Traditional geo-databases face frequent scalability issues while dealing with dense and complex spatial data. Despite plenty of research work in the comparison of relational and NoSQL databases in handling geospatial data, there is a shortage of existing knowledge about the performance of geo-database in a clustered environment like Kubernetes. This paper presents benchmarking of PostgreSQL/PostGIS geospatial databases operating on a clustered environment against non-clustered environments. The benchmarking process considers the average execution times of geospatial structured query language (SQL) queries on multiple hardware configurations to compare the environments based on handling computationally expensive queries involving SQL operations and PostGIS functions. The geospatial queries operate on data imported from OpenStreetMap into PostgreSQL/PostGIS. The clustered environment powered by Kubernetes demonstrated promising improvements in the average execution times of computationally expensive geospatial SQL queries on all considered hardware configurations compared to their average execution times in non-clustered environments.

CNN-BASED MULTI-SCALE HIERARCHICAL LAND USE CLASSIFICATION FOR THE VERIFICATION OF GEOSPATIAL DATABASES

Land use is an important piece of information with many applications. Commonly, land use is stored in geospatial databases in the form of polygons with corresponding land use labels and attributes according to an object catalogue. The object catalogues often have a hierarchical structure, with the level of detail of the semantic information depending on the hierarchy level. In this paper, we extend our prior work for the CNN (Convolutional Neural Network)-based prediction of land use for database objects at multiple semantic levels corresponding to different levels of a hierarchical class catalogue. The main goal is the improvement of the classification accuracy for small database objects, which we observed to be one of the largest problems of the existing method. In order to classify large objects using a CNN of a fixed input size, they are split into tiles that are classified independently before fusing the results to a joint prediction for the object. In this procedure, small objects will only be represented by a single patch, which might even be dominated by the background. To overcome this problem, a multi-scale approach for the classification of small objects is proposed in this paper. Using this approach, such objects are represented by multiple patches at different scales that are presented to the CNN for classification, and the classification results are combined. The new strategy is applied in combination with the earlier tiling-based approach. This method based on an ensemble of the two approaches is tested in two sites located in Germany and improves the classification performance up to +1.8% in overall accuracy and +3.2% in terms of mean F1 score.

Expert Knowledge as Basis for Assessing an Automatic Matching Procedure

The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic production such as positional accuracy assessment (PAA). The automation of the PAA of spatial data is based on automated matching procedures between corresponding spatial objects (usually building polygons) from two geospatial databases (GDB), which in turn are related to the quantification of the similarity between these objects. Therefore, assessing the capabilities of these automated matching procedures is key to making automation a fully operational solution in PAA processes. The present study has been developed in response to the need to explore the scope of these capabilities by means of a comparison with human capabilities. Thus, using a genetic algorithm (GA) and a group of human experts, two experiments have been carried out: (i) to compare the similarity values between building polygons assigned by both and (ii) to compare the matching procedure developed in both cases. The results obtained showed that the GA—experts agreement was very high, with a mean agreement percentage of 93.3% (for the experiment 1) and 98.8% (for the experiment 2). These results confirm the capability of the machine-based procedures, and specifically of GAs, to carry out matching tasks.

CALCULATION AND VISUALIZATION OF MAP ROUTES USING QGIS AND PGROUTING SOFTWARE

Recently, new tools have been created for working with geodata, which are used in various fields of human activity. Software for network analysis and routing solutions is of particular importance. The software product pgRouting is an example, distributed under the GPLv2 license. This program extends the capabilities of PostGIS / PostgreSQL geospatial databases. The article discusses the general principles of constructing routes on the graphs of the road network. It describes how to work with the geospatial database and the pgRouting software for building a route. The purpose of the work is to build a correct rout of a road graph in routing areas with a big number of objects and a poorly developed road network. The problem is solved by software pgRouting and QGIS on the basis of the Dijkstra shortest path algorithm, Johnson and Floyd-Warshall algorithms and allows you to solve the traveling salesman problem, and many others. The task is solved by means of software pgRouting и QGIS. As an experiment the article shows the solution for the task in which it is not enough to use only a road graph for building a correct route. Such situations may occur when routing the areas with a big number of objects and a poorly developed road network. In the process of the experiment described in the article it was found out that software pgRouting together with QGIS allows to rather effectively solve the task on calculation and visualization of the shortest route between two points on the map.

A Deep Learning-Based Solution for Large-Scale Extraction of the Secondary Road Network from High-Resolution Aerial Orthoimagery

Secondary roads represent the largest part of the road network. However, due to the absence of clearly defined edges, presence of occlusions, and differences in widths, monitoring and mapping them represents a great effort for public administration. We believe that recent advancements in machine vision allow the extraction of these types of roads from high-resolution remotely sensed imagery and can enable the automation of the mapping operation. In this work, we leverage these advances and propose a deep learning-based solution capable of efficiently extracting the surface area of secondary roads at a large scale. The solution is based on hybrid segmentation models trained with high-resolution remote sensing imagery divided in tiles of 256 × 256 pixels and their correspondent segmentation masks, resulting in increases in performance metrics of 2.7–3.5% when compared to the original architectures. The best performing model achieved Intersection over Union and F1 scores of maximum 0.5790 and 0.7120, respectively, with a minimum loss of 0.4985 and was integrated on a web platform which handles the evaluation of large areas, the association of the semantic predictions with geographical coordinates, the conversion of the tiles’ format and the generation of geotiff results compatible with geospatial databases.

Electronic Navigational Charts: International Standards and Map Projections

Electronic navigational charts (ENCs) are geospatial databases, compiled for the operational use of Electronic Chart Display and Information systems (ECDIS) according to strict technical specifications of the International Hydrographic Organization (IHO). ECDIS is a GIS system designed for marine navigation according to the relevant standards of the International Maritime Organization (IMO). The international standards for ENCs and ECDIS, issued by the IHO and IMO, cover many aspects of the portrayal of ENCs in ECDIS but do not specify or recommend map projections. Consequently, in some cases, the unjustified employment of map projections by the manufacturers has caused certain functional drawbacks and inadequacies. This article reviews, evaluates and supplements the results of earlier studies on the selection of map projections for the depiction of ENCs in ECDIS and proposes a reasonable set of suitable projections with pertinent selection/implementation rules. These proposals took into consideration that ECDIS users (navigators) are not GIS experts or professional cartographers and consequently, the proposed election/implementation rules have to be simple and straightforward.