Automatic Mapping Research Articles

Automatic lithological mapping from potential field data using machine learning: a case study from Mundiyawas-Khera Cu deposit, Rajasthan, India

Automatic lithological mapping from potential field data using machine learning: a case study from Mundiyawas-Khera Cu deposit, Rajasthan, India

Morphostructural mapping of Borealis Planitia, Mercury

ABSTRACT Orbital data from the MESSENGER spacecraft show that a significant portion of Mercury’s northern hemisphere is covered by smooth plains, which are interpreted to be flood volcanic material and/or impact melt. The smooth plains show pervasive tectonic structures and encompass a broad raised bulge of uncertain geophysical interpretation. In this work, we focus on the mapping of all the morphostructures within the northern smooth plains, aiming at providing a useful dataset for further studies about the mapped area. The structural map is obtained through a twofold process: first with an automatic mapping, using an algorithm to identify all the lineaments from a DEM; and second with a visual inspection and classification of the results of the algorithm in a GIS environment. The final maps are drafted at two different scales, 1:300,000 and 1:600,000. With this approach, we mapped and characterized more than fifty thousand lines marking scarps on the surface, creating a database with several morphometric attributes for each of the identified scarps (e.g. length, azimuth, and height), which can be used for geostatistical study of smooth plains tectonics. Our structural map reveals that: (i) the area is broadly dominated by wrinkle ridges, ghost crater assemblages of lineaments, and scarps related to impact crater processes (e.g. radial faults, secondary crater chains, ejecta emplacement) and that (ii) the amount of strain was not evenly accommodated throughout the northern smooth plains.

AGORA: An intelligent system for the anonymization, information extraction and automatic mapping of sensitive documents

Public institutions, such as law enforcement agencies or health centers, have a vast volume of unstructured text documents, e.g. police reports. Currently, before this data can be shared (e.g. with research institutions), it must go through a lengthy and costly human anonymization procedure.This paper addresses this issue by presenting AGORA, a cutting-edge tool that automatically identifies key entities and anonymizes sensitive data in text documents. AGORA has been developed in partnership with the Spanish National Office Against Hate Crimes and validated in the police and medical domains. This tool allows to export both anonymized texts and identified entities to structured files, thus, simplifying its exploitation for analysis purposes. Also, AGORA is capable of plotting the location entities identified in the documents, as well as obtaining and displaying relevant information from their geographical surroundings. Thus, it simplifies the task of generating comprehensive datasets for subsequent data analysis or predictive tasks. Its main goal is to foster cooperation between public institutions and research centers by easing document sharing as well as serving as a foundation for addressing succeeding phases in data science.The paper conducts a comprehensive assessment of the literature on Named Entity Recognition methodologies and technologies. Followed by extensive computational experiments to identify the best configuration for the NER models embedded in AGORA which include both successful state-of-the-art model setups and novelly proposed ones. Finally, the methodology, conclusions and software provided can be easily reused in similar application scenarios.

Seismic-induced permanent displacement estimation combining acceleration and computer vision measurements

In this study, a seismic-induced permanent displacement estimation technique that combines acceleration and computer vision measurements was proposed. The proposed technique involves installing a vision camera and an accelerometer on a target building, with the vision camera capturing the change in the position of surrounding objects after a seismic event to estimate the permanent displacement of the target building. During the installation, the vision camera and accelerometer were vibrated in a controlled manner using a shaker on the target building. Then, in the absence of any artificial target or prior knowledge of the size of natural targets, an automatic scale factor map was generated, which converts a translation of an image (in pixels) into a physical displacement. After an earthquake, a displacement map was constructed to estimate the relative movement of the target building to the natural targets and then clustered using Gaussian mixture modeling. Finally, the undeformed portions of the natural targets were retrieved to estimate the permanent displacement of the target building with respect to the distribution of the natural target movement. Both indoor and outdoor experiments were conducted to validate the performance of the proposed technique. The results demonstrate the improved performance of the proposed technique compared with the previous technique.

Gully Head-Cuts Inventory and Semi-Automatic Gully Extraction Using LiDAR and Topographic Openness—Case Study: Covurlui Plateau, Eastern Romania

The Covurlui Plateau, a subunit of the Moldavian Plateau located in eastern Romania, possesses a high natural agricultural potential, significantly impacted by soil erosion, particularly gully erosion. The only inventory in the Moldavian Plateau that comprises approximately 9000 gullies extracted from topographical maps was conducted during the 90s. Nowadays, with the advent of advanced techniques and geodata, such as GIS software, aerial photograms, high-resolution satellite images, and high-resolution digital elevation models, we aim to achieve an (1) up-to-date comprehensive inventory of gully head-cuts and (2) a very detailed mapping of the spatial distribution of gullied lands. Firstly, the gully head-cuts were inventoried for the entire region using platforms such as Google, Esri, and Bing, through the QuickMapService plugin within QGIS 3.16 software, with the assistance of Landsat and Sentinel satellite images. Secondly, the automatic mapping of gullies was carried out using a 5 m high-resolution Digital Elevation Model and the Topographic Openness module offered by SAGA GIS software through QGIS software. As a result, we identified 5868 gully head-cuts for the Covurlui Plateau, with an average density of 2.57 gully head-cuts per square kilometer. Additionally, the identified gullies occupy over 3570 hectares, representing 1.57% of the total area. Overall, the topographic openness index proves to be an efficient tool for the semi-automatic extraction of gullies from high-resolution digital elevation models.

StainView: A Fast and Reliable Method for Mapping Stains in Facades Using Image Classification in HSV and CIELab Colour Space

The new Construction 4.0 paradigm takes advantage of existing technologies. In this scope, the development and application of image-based methods for evaluating and monitoring the state of conservation of buildings has shown significant growth, including support for maintenance plans. Recently, powerful algorithms have been applied to automatically evaluate the state of conservation of buildings using deep learning frameworks, which are utilised as a black-box approach. The large amount of data required for training, the difficulty in generalising, and the lack of parameters to assess the quality of the results often make it difficult for non-experts to evaluate them. For several applications and scenarios, simple and more intuitive image-based approaches can be applied to support building inspections. This paper presents the StainView, which is a fast and reliable method. The method is based on the classification of the mosaic image, computed from a systematic acquisition, and allows one to (i) map stains in facades; (ii) locate critical areas; (iii) identify materials; (iv) characterise colours; and (v) produce detailed and comprehensive maps of results. The method was validated in three identical buildings in Bairro de Alvalade, in Lisbon, Portugal, that present different levels of degradation. The comparison with visual inspection demonstrates that StainView enables the automatic location and mapping of critical areas with high efficiency, proving to be a useful tool for building inspection: differences were of approximately 5% for the facade with the worst and average state of conservation, however, the values deteriorate for the facade under good conditions, reaching the double of percentage. In terms of processing speed, StainView allows a facade mapping that is 8–12 times faster, and this difference tends to grow with the number of evaluated façades.

MAP3D: An explorative approach for automatic mapping of real-world eye-tracking data on a virtual 3D model.

Mobile eye tracking helps to investigate real-world settings, in which participants can move freely. This enhances the studies' ecological validity but poses challenges for the analysis. Often, the 3D stimulus is reduced to a 2D image (reference view) and the fixations are manually mapped to this 2D image. This leads to a loss of information about the three-dimensionality of the stimulus. Using several reference images, from different perspectives, poses new problems, in particular concerning the mapping of fixations in the transition areas between two reference views. A newly developed approach (MAP3D) is presented that enables generating a 3D model and automatic mapping of fixations to this virtual 3D model of the stimulus. This avoids problems with the reduction to a 2D reference image and with transitions between images. The x, y and z coordinates of the fixations are available as a point cloud and as .csv output. First exploratory application and evaluation tests are promising: MAP3D offers innovative ways of post-hoc mapping fixation data on 3D stimuli with open-source software and thus provides cost-efficient new avenues for research.

Review on the FPGA design optimization for ASIC

FPGA is a product of further development on programmable devices such as PAL and GAL. It is not only a chip, but a design pattern. With the rapid growth in FPGA performance and density, its complete top-down model can be applied to the development of similar ASICs while improving the technology itself. This paper shows that a summary analysis of previous articles can conclude that the application of FPGA design to ASICs can lead to optimization of many aspects. Architecture-level embedding and mapping transformation are the current mainstream application methods, both of which can bring strong economic and time benefits. Meanwhile, there are various issues that need special consideration in the application process which is related to the question of whether or not to map and convert. Finally, according to the analysis, we still need to solve two important issues which are the lack of automatic mapping tools and the lack of a unified function library in the future.

An Automatic Method for Rice Mapping Based on Phenological Features with Sentinel-1 Time-Series Images

Rice is one of the most important staple foods in the world, feeding more than 50% of the global population. However, rice is also a significant emitter of greenhouse gases and plays a role in global climate change. As a result, quickly and accurately obtaining the rice mapping is crucial for ensuring global food security and mitigating global warming. In this study, we proposed an automated rice mapping method called automated rice mapping using V-shaped phenological features of rice (Auto-RMVPF) based on the time-series Sentinel-1A images, which are composed of four main steps. First, the dynamic threshold method automatically extracts abundant rice samples by flooding signals. Second, the second-order difference method automatically extracts the phenological period of rice based on the scattering feature of rice samples. Then, the key “V” feature of the VH backscatter time series, which rises before and after rice transplanting due to flooding, is used for rice mapping. Finally, the farmland mask is extracted to avoid interference from non-farmland features on the rice map, and the median filter is applied to remove noise from the rice map and obtain the final spatial distribution of rice. The results show that the Auto-RMVPF method not only can automatically obtain abundant rice samples but also can extract the accurate phenological period of rice. At the same time, the accuracy of rice mapping is also satisfactory, with an overall accuracy is more than 95% and an F1 score of over 0.91. The overall accuracy of the Auto-RMVPF method is improved by 2.8–12.2% compared with support vector machine (SVM) with an overall accuracy of 89.9% (25 training samples) and 92.2% (124 training samples), random forest (RF) with an overall accuracy of 82.8% (25 training samples) and 88.3% (124 training samples), and automated rice mapping using synthetic aperture radar flooding signals (ARM-SARFS) with an overall accuracy of 89.9%. Altogether, these experimental results suggest that the Auto-RMVPF method has broad prospects for automatic rice mapping, especially for mountainous regions where ground samples are often not easily accessible.

Automating the mapping of course learning outcomes to program learning outcomes using natural language processing for accurate educational program evaluation

Quality control and assurance plays a fundamental role within higher education contexts. One means by which quality control can be performed is by mapping the course learning outcomes (CLOs) to the program learning outcomes (PLO). This paper describes a system by which this mapping process can be automated and validated. The proposed AI-based system automates the mapping process through the use of natural language processing. The framework underwent testing using two actual datasets from two educational programs, and the findings were promising. A testament to the potential of the suggested framework was the precision of the mapping detected (83.1% and 88.1% for the two programs, respectively) compared to the mapping performed by the domain experts. A web-based tool was created to help teachers and administrators execute automatic mappings ( https://dsaluaeu.github.io/mapper.html ). The data and software used in this research project can be found at the following URL: https://github.com/nzaki02/CLO-PLO .

Automated identification and mapping of geological folds in cross sections

Abstract Cross sections carry information on the spatial distribution of rock strata and the development of geological structures, and it is an important data source for three-dimensional (3D) geological modeling. However, the interpretation and mapping of geological structures in sections by means of manual interpretation are inefficient and costly, and the performance varies greatly with the experts’ ability and experience. The objective of this article is to develop an automatic recognition and mapping method for folds in cross sections. This method mainly includes identifying folds based on stratigraphic sequence characteristics (symmetrical and repetitive), classifying fold types based on geometric attributes of folds (interval scheduling, strike, and section morphology), optimizing strata based on the superposition principle and area conservation principle, and constructing the polygon features of folds. Based on experiments in the Parallel Fold Belt of Eastern Sichuan and the central Appalachian fold-thrust belt in the Appalachian Mountains, the method presented in this article can effectively be used for automatic recognition and high-quality mapping of folds in the cross sections. The method provides a good source of geological cross-sectional data for the 3D modeling of geologic bodies.

Review of Artificial Intelligence Applications for Virtual Sensing of Underground Utilities.

Accurately identifying the location and depth of buried utility assets became a considerable challenge in the construction industry, for which accidental strikes can cause important economic losses and safety concerns. While the collection of as-built utility locations is becoming more accurate, there still exists an important need to be capable of accurately detecting buried utilities in order to eliminate risks associated with digging. Current practices typically involve the use of trained agents to survey and detect underground utilities at locations of interest, which is a costly and time-consuming process. With advances in artificial intelligence (AI), an opportunity arose in conducting virtual sensing of buried utilities by combining robotics (e.g., drones), knowledge, and logic. This paper reviewed methods that are based on AI in mapping underground infrastructure. In particular, the use of AI in aerial and terrestrial mapping of utility assets was reviewed, followed by a summary of AI techniques used in fusing multi-source data in creating underground infrastructure maps. Key observations from the consolidated literature were that (1) when leveraging computer vision methods, automatic mapping techniques vastly focus on manholes localized from aerial imagery; (2) when applied to non-intrusive sensing, AI methods vastly focus on empowering ground-penetrating radar (GPR)-produced data; and (3) data fusion techniques to produce utility maps should be extended to any utility assets/types. Based on these observations, a universal utility mapping model was proposed, one that could enable mapping of underground utilities using limited information available in the form of different sources of data and knowledge.

The Recursive Set-Subset Ordering Principle orders adjectives and relative clauses in visual context

We present evidence from two picture-matching experiments that native adult speakers of American English order and interpret sequences of nouns modified by adjectives/relative clauses containing adjectives in line with the Recursive Set-Subset Ordering Principle proposed in Bleotu & Roeper (2022a, b) and Bleotu, Foucault, & Roeper (2023). This principle ensures an automatic mapping of set-subset semantics to a recursive syntax in Universal Grammar, such that set modifiers are merged closer to the noun than subset modifiers: leaves (N) that are short (SET) that are long (SUBSET) or short (SET) leaves (N) that are long (SUBSET). We here expand upon the interpretation and ordering of set-subset modification to elucidate the syntactic, semantic and pragmatic interface.

Construction technology of graph and model mutual capacity in smart substation

In order to meet the requirements of automatic mapping of the smart substation models, based on the application requirements of the smart substation model construction and automatic model association configuration, the automatic generation technology of smart station diagram models is studied, and the automatic correlation modeling method and wiring diagram generation technology of primary and secondary equipment models based on the automatic matching of predefined correlation models are proposed. Support the realization of the goal of effective configuration and efficient application of the secondary system model of the smart substation.

A novel solution for extracting individual tree crown parameters in high-density plantation considering inter-tree growth competition using terrestrial close-range scanning and photogrammetry technology

Automatic tree crown mapping, crown width (CW) and crown projection area (CPA) extraction are the basis for high-precision estimation of forest productivity and carbon stock. Terrestrial close-range observation (TCRO) technology, mainly including terrestrial laser scanning (TLS) and close-range photogrammetry (CRP), measures individual tree structural parameters by generating detailed three-dimensional (3D) points, making it a potential replacement for labor-intensive manual forest inventories. However, accurate extraction of crown parameters using TCRO in high-density forests remain a challenge due to different degrees of missing crown information caused by occlusion between trees and limitations of computer 3D modeling techniques and segmentation methods. Here, we propose a model for estimating crown parameters using tree spacing, diameter at breast height (DBH) and stand age, which collaboratively considers the advantages of TCRO data and the bioecological mechanisms of inter-tree competition. First, an approach to construct a tree triangulation network with tree position and tree spacing is designed based on the analysis of the bioecological mechanisms of inter-tree competition relationships. Second, a model to quantify inter-tree competition using tree spacing, DBH and stand age is developed to extract CW and CPA. We demonstrate the generality of our model to high-density forests in subtropical China with TLS data. It shows a good performance in Chinese fir, Eucalyptus and Spingbract Chinkapin-dominated plots with estimation accuracy (EA) ≥ 90.22 % and relative root mean square error (rRMSE) ≤ 0.1286 for CW and EA ≥ 84.51 % and rRMSE ≤ 0.1861 for CPA. In addition, the performance in estimating CW (EA = 89.80 %) and CPA (EA = 86.13 %) using CRP point clouds demonstrate the applicability of our model to CRP. The proposed model collaboratively considers inter-tree competition mechanism and TCRO data characteristics, therefore it is interpretable in terms of bioecological principle and universal for different forest environments (e.g., coniferous, broad-leaved and mixed coniferous species). The results show that the method provides an efficient solution for automatic and accurate measurements of the crown parameters in the forest sample plot investigation, which can support the fine forest management and carbon stock estimation.

Automatic Mapping of Physical Urban Problems Using Remotely Sensed Imagery

While big cities are expected to exercise cost-effective, evidence-based planning, many are under reactive management, facing simultaneous problems and limited resources. This project develops a proof-of-concept workflow for the automatic monitoring of physical urban problems by combining remote sensing for detection and cartography for visualization. The example problem treated was the obstructive parking of vehicles on pavements as proxy for restricted urban mobility. Nine aerial images of UK urban areas were processed by a deep learning object detector of standard cars, achieving an F-score of 70.72%. Two large scale map reports of 200m wide areas were produced, featuring car detections and overlaps with topographic mapping features. Complementary analysis included the calculation of total detection window overlap per roadside pavement and its change with time. The proposed method combines uniform city-wide coverage with fast interpretation and can inspire the development of professional urban planning tools.

Mean Inflection Point Distance: Artificial Intelligence Mapping Accuracy Evaluation Index—An Experimental Case Study of Building Extraction

Mapping is a fundamental application of remote sensing images, and the accurate evaluation of remote sensing image information extraction using artificial intelligence is critical. However, the existing evaluation method, based on Intersection over Union (IoU), is limited in evaluating the extracted information’s boundary accuracy. It is insufficient for determining mapping accuracy. Furthermore, traditional remote sensing mapping methods struggle to match the inflection points encountered in artificial intelligence contour extraction. In order to address these issues, we propose the mean inflection point distance (MPD) as a new segmentation evaluation method. MPD can accurately calculate error values and solve the problem of multiple inflection points, which traditional remote sensing mapping cannot match. We tested three algorithms on the Vaihingen dataset: Mask R-CNN, Swin Transformer, and PointRend. The results show that MPD is highly sensitive to mapping accuracy, can calculate error values accurately, and is applicable for different scales of mapping accuracy while maintaining high visual consistency. This study helps to assess the accuracy of automatic mapping using remote sensing artificial intelligence.

BuildSeg: A General Framework for the Segmentation of Buildings

Building segmentation from aerial images and 3D laser scanning (LiDAR) is a challenging task due to the diversity of backgrounds, building textures, and image quality. While current research using different types of convolutional and transformer networks has considerably improved the performance on this task, more precise and accurate segmentation methods for buildings are desirable for applications such as automatic mapping. In this study, we propose a general framework termed BuildSeg employing a generic approach that can be quickly applied to segment buildings. Different data sources were combined to increase generalization performance.
 The approach yields good results for different data sources as shown by experiments on high-resolution multi-spectral and LiDAR imagery of cities in Norway, Denmark, and France.
 We applied ConvNeXt and SegFormer-based models on the high-resolution aerial image dataset from the MapAI-competition. The methods achieved an IoU of 0.7902 and a boundary IoU of 0.6185 on the test set. We used post-processing to account for the rectangular shape of the objects.This increased the boundary IOU from 0.6185 to 0.6189. The code is publicly available.
 

Vectorial and topologically valid segmentation of forestry road networks from ALS data

Accurate information on road location is critical for forest management and conservation strategies. Road location data supports the analysis of road accessibility and usability and is a critical information layer for forest harvest, financial planning, wildfire suppression, and protection activities. The global expanse of forests, their remoteness, and difficulty to access have necessitated the development of automatic or semi-automatic remote sensing methodologies to map roads using passive optical imagery or Airborne Laser Scanning (ALS). Conventional automatic road mapping methods are raster-based and map roads as patches of disconnected pixels. This paper addresses the limitations of raster-based automatic forest road extraction and presents a method for producing a topologically accurate vectorial road network. Our method, presented as a fully documented and open-source software tool, uses metrics derived from an ALS point cloud to produce a raster of road conductivity. From this conductivity raster, the method “drives” the roads iteratively by detecting and following road intersections. We demonstrate the method’s efficacy using a road network in Quebec, Canada, where 96% of the roads in a binary raster, and 84% using our probability map, are vectorized properly from an ALS point cloud with 4% false positives. Our proposed method may significantly reduce the training requirements of machine learning techniques used to classify roads by being very robust to false positive and false negative classifications.

Developing an intelligent cloud attention network to support global urban green spaces mapping

Urban green spaces (UGS) play an important role in understanding of urban ecosystems, climate, environment, and public health concerns. Satellite derived UGS maps provide an efficient and effective tool for urban studies and contribute to targets and indicators of the sustainable development goals, at the global level, set by the United Nations. However, clouds create a challenging issue in optical satellite image processing, leading to significant uncertainty in UGS mapping. In this study, we propose an automatic UGS mapping method by integrating satellite images with crowdsourced geospatial data while aiming to reduce the uncertainty caused by cloud contamination. The proposed method consists of three parts: (1) auxiliary data pre-processing module; (2) cloud attention intelligent network (CAI-net); and (3) non-cloud scenes classification module. The auxiliary data pre-processing module was used to convert crowdsourcing geospatial data into auxiliary maps. The CAI-net was proposed to retrieve detailed UGS classes within clouds from satellite image patches and auxiliary maps, while non-cloud scenes classification module was used to extract UGS from satellite image patches. The proposed method was applied to generate spatial continuous global UGS map products, considering the uncertainty caused by cloud contamination. The results show the proposed method yielded a high-quality global UGS map with average overall accuracy as high as 92.96% when satellite images had cloud coverage ranging from 0% to 50%. The geospatial AI, specifically CAI-net, can provide more accurate UGS mapping regardless of different geographical and climatic conditions of the study areas, which is especially significant for humid tropical and subtropical regions with frequent clouds and rains.