Geospatial Image Processing Research Articles

Recognizing the Wadi Fluvial Structure and Stream Network in the Qena Bend of the Nile River, Egypt, on Landsat 8-9 OLI Images

With methods for processing remote sensing data becoming widely available, the ability to quantify changes in spatial data and to evaluate the distribution of diverse landforms across target areas in datasets becomes increasingly important. One way to approach this problem is through satellite image processing. In this paper, we primarily focus on the methods of the unsupervised classification of the Landsat OLI/TIRS images covering the region of the Qena governorate in Upper Egypt. The Qena Bend of the Nile River presents a remarkable morphological feature in Upper Egypt, including a dense drainage network of wadi aquifer systems and plateaus largely dissected by numerous valleys of dry rivers. To identify the fluvial structure and stream network of the Wadi Qena region, this study addresses the problem of interpreting the relevant space-borne data using R, with an aim to visualize the land surface structures corresponding to various land cover types. To this effect, high-resolution 2D and 3D topographic and geologic maps were used for the analysis of the geomorphological setting of the Qena region. The information was extracted from the space-borne data for the comparative analysis of the distribution of wadi streams in the Qena Bend area over several years: 2013, 2015, 2016, 2019, 2022, and 2023. Six images were processed using computer vision methods made available by R libraries. The results of the k-means clustering of each scene retrieved from the multi-temporal images covering the Qena Bend of the Nile River were thus compared to visualize changes in landforms caused by the cumulative effects of geomorphological disasters and climate–environmental processes. The proposed method, tied together through the use of R scripts, runs effectively and performs favorably in computer vision tasks aimed at geospatial image processing and the analysis of remote sensing data.

Application of a CNN to the Boda Claystone Formation for high-level radioactive waste disposal

Nations relying on nuclear power generation face great responsibilities when designing their firmly secured final repositories. In Hungary, the potential host rock [the Boda Claystone Formation (BCF)] of the deep geological repository is under extensive examination. To promote a deeper comprehension of potential radioactive isotope transport and ultimately synthesis for site evaluation purposes, we have efficiently tailored geospatial image processing using a convolutional neural network (CNN). We customized the CNN according to the intricate nature of the fracture geometries in the BCF, enabling the recognition process to be particularly sensitive to details and to interpret them in the correct tectonic context. Furthermore, we set the highest processing scale standards to measure the performance of our model, and the testing circumstances intentionally involved various technological and geological hindrances. Our presented model reached ~ 0.85 precision, ~ 0.89 recall, an ~ 0.87 F1 score, and a ~ 2° mean error regarding dip value extraction. With the combination of a CNN and geospatial methodology, we present the description, performance, and limits of a fully automated workflow for extracting BCF fractures and their dipping data from scanned cores.

GeoAI at ACM SIGSPATIAL

Geospatial artificial intelligence (GeoAI) is an interdisciplinary field that has received tremendous attention from both academia and industry in recent years. This article reviews the series of GeoAI workshops held at the Association for Computing Machinery (ACM) International Conference on Advances in Geographic Information Systems (SIGSPATIAL) since 2017. These workshops have provided researchers a forum to present GeoAI advances covering a wide range of topics, such as geospatial image processing, transportation modeling, public health, and digital humanities. We provide a summary of these topics and the research articles presented at the 2017, 2018, and 2019 GeoAI workshops. We conclude with a list of open research directions for this rapidly advancing field.

Environmental Assessment in Neotropical Watersheds: A Multi-Factorial Approach

This study evaluated the environmental conditions of two watersheds selected on the basis of similarities in land use. The environmental conditions of the watersheds were analyzed using two geoprocessing methods and in situ evaluations by applying the rapid assessment protocol (RAP). Geospatial image processing was used to analyze land use, slope, soil classification, and rainfall, while RAP was used to evaluate the connectivity and size of riparian corridors. The results revealed varied uses of land with the landscape matrix in both watersheds, composed of agriculture, pasture, and urban centers. Urban centers were defined as spots and riparian zones as corridors. The analysis of environmental fragility considering all the geospatial variables, classified both watersheds as having medium fragility. The most fragile sites were the urban centers, which had a high slope and degraded riparian zone. Onsite assessments have shown that corridors do not have the size required by legislation; they are fragmented and unstructured, and they contain exotic species. We also propose that a multi-factorial approach be used to evaluate watersheds, associating geospatial assessments and onsite analyses that consider the limitations pointed out in each protocol. This reduces sampling errors and shows the actual state of conservation in riparian zones.

A Performance Evaluation of a Geo-Spatial Image Processing Service Based on Open Source PaaS Cloud Computing Using Cloud Foundry on OpenStack

Recently, web application services based on cloud computing technologies are being offered. In the web-based application field of geo-spatial data management or processing, data processing services are produced or operated using various information communication technologies. Platform-as-a-Service (PaaS) is a type of cloud computing service model that provides a platform that allows service providers to implement, execute, and manage applications without the complexity of establishing and maintaining the lower-level infrastructure components, typically related to application development and launching. There are advantages, in terms of cost-effectiveness and service development expansion, of applying non-proprietary PaaS cloud computing. Nevertheless, there have not been many studies on the use of PaaS technologies to build geo-spatial application services. This study was based on open source PaaS technologies used in a geo-spatial image processing service, and it aimed to evaluate the performance of that service in relation to the Web Processing Service (WPS) 2.0 specification, based on the Open Geospatial Consortium (OGC) after a test application deployment using the configured service supported by a cloud environment. Using these components, the performance of an edge extraction algorithm on the test system in three cases, of 300, 500, and 700 threads, was assessed through a comparison test with another test system, in the same three cases, using Infrastructure-as-a-Service (IaaS) without Load Balancer-as-a-Service (LBaaS). According to the experiment results, in all the test cases of WPS execution considered in this study, the PaaS-based geo-spatial service had a greater performance and lower error rates than the IaaS-based cloud without LBaaS.

UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring.

Recent advances in unmanned aerial system (UAS) sensed imagery, sensor quality/size, and geospatial image processing can enable UASs to rapidly and continually monitor coral reefs, to determine the type of coral and signs of coral bleaching. This paper describes an unmanned aerial vehicle (UAV) remote sensing methodology to increase the efficiency and accuracy of existing surveillance practices. The methodology uses a UAV integrated with advanced digital hyperspectral, ultra HD colour (RGB) sensors, and machine learning algorithms. This paper describes the combination of airborne RGB and hyperspectral imagery with in-water survey data of several types in-water survey of coral under diverse levels of bleaching. The paper also describes the technology used, the sensors, the UAS, the flight operations, the processing workflow of the datasets, the methods for combining multiple airborne and in-water datasets, and finally presents relevant results of material classification. The development of the methodology for the collection and analysis of airborne hyperspectral and RGB imagery would provide coral reef researchers, other scientists, and UAV practitioners with reliable data collection protocols and faster processing techniques to achieve remote sensing objectives.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data.

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analysing and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

Linkage of OGC WPS 2.0 to the e-Government Standard Framework in Korea: An Implementation Case for Geo-Spatial Image Processing

There are many cases wherein services offered in geospatial sectors are integrated with other fields. In addition, services utilizing satellite data play important roles in daily life and in sectors such as environment and science. Therefore, a management structure appropriate to the scale of the system should be clearly defined. The motivation of this study is to resolve issues, apply standards related to a target system, and provide practical strategies with a technical basis. South Korea uses the e-Government Standard Framework, using the Java-based Spring framework, to provide guidelines and environments with common configurations and functions for developing web-based information systems for public services. This web framework offers common sources and resources for data processing and interface connection to help developers focus on business logic in designing a web system. In this study, a geospatial image processing system—linked with the Open Geospatial Consortium (OGC) Web Processing Service (WPS) 2.0 standard for real geospatial information processing, and based on this standard framework—was designed and built utilizing fully open sources. This is the first case of implementation based on WPS 2.0 running on the e-Government Standard Framework. Establishing a standard for its use will be important, and the system built in this study can serve as a reference for the foundational architecture in building geospatial web service systems with geodata-processing functionalities in government agencies.

Updating land cover databases using a single very high resolution satellite image

Abstract. Image change detection has been extensively tackled in the literature in various domains, and in particular for remote sensing purposes. Indeed, very high resolution geospatial images are nowadays ubiquitous and can be used to update existing 2D and 3D geographical databases. Such databases can be projected into the image space, by a rasterization step. Therefore, they provide 2D label maps that can be subsequently compared with classifications resulting for geospatial image processing. In this paper, we propose a classificationbased method to detect changes between label maps created from 2D land-cover databases and an more recent orthoimage, without any prior assumptions about the databases composition. Our supervised method is based both on an efficient training set selection and a hierarchical decision process, that follows the structure of topographical databases. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates, a standard limitation of existing approaches. The designed framework is successfully applied on very high resolution images of Pl´eiades sensor and two distinct national land-cover databases.

공간 영상 처리를 위한 SIFT 매칭 기법의 성능 분석

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

Voronoi Image Segmentation and Its Applications to Geoinformatics

As various geospatial images are available for analysis, there is a strong need for an intelligent geospatial image processing method. Segmenting and districting digital images is a core process and is of great importance in many geo-related applications. We propose a flexible image segmentation framework based on generalized Voronoi diagrams through Euclidean distance transforms. We introduce a three-scan algorithm that segments images in O(N) time when N is the number of pixels. The algorithm is capable of handling generators of complex types (point, line and area), Minkowski metrics and different weights. This paper also provides applications of the proposed method in various geoinformation datasets. Illustrated examples demonstrate the usefulness and robustness of our proposed method.