Geospatial Image Research Articles

Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi’s domain adaptability

Research on geospatial foundation models (GFMs) has become a trending topic in geospatial artificial intelligence (AI) research due to their potential for achieving high generalizability and domain adaptability, reducing model training costs for individual researchers. Unlike large language models, such as ChatGPT, constructing visual foundation models for image analysis, particularly in remote sensing, encountered significant challenges such as formulating diverse vision tasks into a general problem framework. This paper evaluates the recently released NASA-IBM GFM Prithvi for its predictive performance on high-level image analysis tasks across multiple benchmark datasets. Prithvi was selected because it is one of the first open-source GFMs trained on time-series of high-resolution remote sensing imagery. A series of experiments were designed to assess Prithvi’s performance as compared to other pre-trained task-specific AI models in geospatial image analysis. New strategies, including band adaptation, multi-scale feature generation, and fine-tuning techniques, are introduced and integrated into an image analysis pipeline to enhance Prithvi’s domain adaptation capability and improve model performance. In-depth analyses reveal Prithvi’s strengths and weaknesses, offering insights for both improving Prithvi and developing future visual foundation models for geospatial tasks.

Leveraging Pre-trained Deep Learning Models for Remote Sensing Image Classification: A Case Study with ResNet50 and EfficientNet

The procedure of categorizing images from remote sensing is also another application of machine learning not just ground-based platforms (for instance satellites), aerial platforms become platforms sometimes in aviation either. They erase the counterparts that were based on individual categories and are portrayed on a specific part of the image. Geospatial Supply of gravel mainly is used for producing railway track, road and concrete surface. Data by analyzing their buildup, dams, bridges, extraordinary open spaces, reservoirs and canals. It targets to be specific and exact as possible in a different specific area of the land. Aspects of the enlarged portrait or distinctions weaved into the completed arts. This might have aspects such as mapping of the trees, plants, rivers, cities, farms and woodlands, and other items. Geospatial image classification is necessary for the identification and real-time analysis of different hazards and unrests. Provide numerous applications, including waste management, water resources, air quality, and traffic control in the urban contexts. Planning, monitoring the environment, land cover, mapping, as well as post-disaster recovery. Management team, traffic control, and situation assessments. In the past, human experts situated in a selected area classified geographical images by means of manual processing. One that involved the allocation of too much time. As this is one of the two broad categories, how to get rid of it is consequently. Applying machine learning and deep learning methods we analyze and interpret the data in order to reduce the time required to provide feedback which allows the system to reach a higher accuracy. The procedure will also be more reliable and the outcome will hopefully be more efficient CNNs are one of the deep learning subclasses in which the network learns and improves without the need for human intervention. It extracts features from images. They are main for the performance and metrics to help the organization to decide on whether they have accomplished their goals, using visual imagery.

Georectifying drone image data over water surfaces without fixed ground control: Methodology, uncertainty assessment and application over an estuarine environment

Light-weight consumer-grade drones have the potential to provide geospatial image data to study a broad range of oceanic processes. However, rigorously tested methodologies to effectively and accurately geolocate and rectify these image data over mobile and dynamic water surfaces, where temporally fixed points of reference are unlikely to exist, are limited. We present a simple to use automated workflow for georectifying individual aerial images using position and orientation data from the drone's on-board sensor (i.e. direct-georectification). The presented methodology includes correcting for camera lens distortion and viewing angle and exploits standard mathematics and camera data processing techniques. The method is used to georectify image datasets from test flights with different combinations of altitude and camera angle. Using a test site over land, directly-georectified images, as well as the same images georectified using standard photogrammetry software, are evaluated using a network of known ground control points. The novel methodology performs well with the camera at nadir (both 10 m and 25 m above ground level) and exhibits a mean spatial accuracy of ±1 m. The same accuracy is achieved when the camera angle is 30° at 10 m above ground level but decreases to ±2.9 m at 30° and 25 m. The accuracy changes because the uncertainties are a function of the altitude and angle of the camera versus the ground. Drone in-flight positioning errors can reduce the accuracy further to ±5 m with the camera at 30° and 25 m. An ensemble approach is used to map the uncertainties within the camera field-of-view to show how they change with viewing distance and drone position and orientation. The complete approach is demonstrated over an estuarine environment that includes the shoreline and open water, producing results consistent with the land-based field-tests of accuracy. Overall, the workflow presented here provides a low cost and agile solution for direct-georectification of drone-captured image data over water surfaces. This approach could be used for collecting and processing image data from drones or ship-mounted cameras to provide observations of ocean colour, sea-ice, ocean glitter, sea surface roughness, white-cap coverage, coastal water quality, and river plumes. The Python scripts for the complete image georectification workflow, including uncertainty map generation, are available from https://github.com/JamieLab/SArONG.

Mapping soil trace metal distribution using remote sensing and multivariate analysis.

Trace metal soil contamination poses significant risks to human health and ecosystems, necessitating thorough investigation and management strategies. Researchers have increasingly utilized advanced techniques like remote sensing (RS), geographic information systems (GIS), geostatistical analysis, and multivariate analysis to address this issue. RS tools play a crucial role in collecting spectral data aiding in the analysis of trace metal distribution in soil. Spectroscopy offers an effective understanding of environmental contamination by analyzing trace metal distribution in soil. The spatial distribution of trace metals in soil has been a key focus of these studies, with factors influencing this distribution identified as soil type, pH levels, organic matter content, land use patterns, and concentrations of trace metals. While progress has been made, further research is needed to fully recognize the potential of integrated geospatial imaging spectroscopy and multivariate statistical analysis for assessing trace metal distribution in soils. Future directions include mapping multivariate results in GIS, identifying specific anthropogenic sources, analyzing temporal trends, and exploring alternative multivariate analysis tools. In conclusion, this review highlights the significance of integrated GIS and multivariate analysis in addressing trace metal contamination in soils, advocating for continued research to enhance assessment and management strategies.

An approach to dynamic visualization of heterogeneous geospatial vector images

One of the well-known problems of geoinformation software libraries in the visualization of geospatial data is the low efficiency and limited frame-by-frame change of a group of spatial layers with time reference. Among the most significant challenges of visualization is that the synchronous switching of a group of spatial layers is impossible to perform, which leads to less accurate estimates of the temporal anisotropy of the corresponding spatial data. The problem is further exacerbated by the heterogeneity of spatial information, which is expressed in sampling steps, formats used by geospatial primitives. The said problem significantly complicates the analysis of spatiotemporal information in many research and application areas. An illustrative example may be found in the problem of analyzing the spatiotemporal anisotropy of geophysical information. Here, for the visualization to be implemented, the dynamic evaluation of the retrospective data in a given time interval needs to performed. The paper proposes an approach that enables heterogeneous vector geospatial data to be integrated before subsequent processing, analysis and visualization. The effectiveness of the developed approach is confirmed by the example of a web application for visualizing a geospatial image as an array of spatial polylines of arbitrary data, as well as in the problem of analysis of spatiotemporal variations of geophysical data.

Hybrid ResNet152-EML model for Geo-spatial image classification

Hybrid ResNet152-EML model for Geo-spatial image classification

Enhanced Port Vulnerability Assessment Using Unmanned-Aerial-Vehicle-Based Structural Health Monitoring

Port vulnerability assessment is inherently linked to the delivery of sustainable and resilient infrastructure. Identifying the vulnerabilities and weaknesses of a port system allows for the minimization of disaster effects and optimization of maintenance, repair, or mitigation actions. The current port vulnerability assessment practices are built upon the examination of a diversity of indicators (parameters), including technical, physical, environmental, and socioeconomic pressures. From an engineering perspective, and given that ports are tangible infrastructure assets, their vulnerability is highly affected by the structural condition of their facilities. Hence, the present research seeks to enhance port vulnerability assessment by introducing structural condition parameters based on Structural Health Monitoring applications. The four fishing and leisure harbors of the Municipality of Thebes, located in central Greece, were used as a case study. Two approaches were considered for the harbors’ vulnerability assessments: (a) enabling and (b) disabling the use of the proposed parameters. In situ inspections were conducted with the employment of an Unmanned Aerial Vehicle (UAV) for condition monitoring. UAV data were analyzed to generate geospatial images that allow for the mapping and detecting of defects and failures in port infrastructure. The overall research assists decision-makers in gaining valuable insight into the system’s vulnerabilities and prioritizing their interventions.

Conformal Test Martingale-Based Change-Point Detection for Geospatial Object Detectors

Unsupervised domain adaptation for object detectors addresses the problem of improving the cross-domain robustness of object detection from label-rich to label-poor domains, which has been explored in many studies. However, one important issue in terms of when to apply the domain adaptation algorithm for geospatial object detectors has not been fully considered in the literature. In this paper, we tackle the problem of detecting the moment or change-point when the domain of geospatial images changes based on conformal test martingale. Beyond the simple introduction of this martingale-based process, we also propose a novel transformation approach to the original conformal test martingale to make change-point detection more efficient. The experiments are conducted with two partitions of our released large-scale remote sensing dataset and the experimental results empirically demonstrate the promising effectiveness and efficiency of our proposed algorithms for change-point detection.

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.

A geospatial image based eye movement dataset for cartography and GIS

ABSTRACT Eye movement is a new type of data for cartography and geographic information science (GIS) research. However, previous studies rarely built eye movement datasets with geospatial images. In this paper, we firstly proposed a geospatial image-based eye movement dataset called GeoEye, a publicly shared, widely available eye movement dataset. This dataset consists of 110 college-aged participants who freely viewed 500 images, including thematic maps, remote sensing images, and street view images. In addition, we used the dataset for geospatial image saliency prediction and map user identification. Results demonstrated the scientific benefits and applications of the proposed dataset. GeoEye dataset will not only promote the application of eye-tracking data in cartography and GIS research but also intelligence and customization of geographic information services.

GSGAN: Learning controllable geospatial images generation

Compared with natural images, geospatial images cover larger area and have more complex image contents. There are few algorithms for generating controllable geospatial images, and their results are of low quality. In response to this problem, this paper proposes Geospatial Style Generative Adversarial Network to generate controllable and high-quality geospatial images. Current conditional generators suffer the mode collapse problem in geospatial field. The problem is addressed via a modified mode seeking regularization term with contrastive learning theory. Besides, the discriminator network architecture is modified to process global feature information and texture information of geospatial images. Feature loss in the generator is introduced to stabilize the training process and improve generated image quality. Comprehensive experiments are conducted on UC Merced Land Use Dataset, NWPU-RESISC45 Dataset, and AID Dataset to evaluate all compared methods. Experiment results show our method outperforms state-of-the-art models. Our method not only generates high-quality and controllable geospatial images, but also enhances the discriminator to learn better representations.

Geospatial Evaluation of Building Facility in Oyo State, Southwestern Nigeria

Geospatial image acquisitions through the Unmanned Aerial Vehicle (UAV) techniques has been used geospatially to assess the Infrastructural building roof facilities of Emmanuel Alayande College of Education Campus, Oyo State, Nigeria. Unmanned Aerial Vehicle (UAV) is an aircraft without a human pilot on board, it is a veritable tool to solve numerous problems socially, and spatially which serves as an effective means of search, detection and identifying objects and the subjects of interest, as well as the precise determination of their coordinates. Drone deploy flight planning software were also used during this study. This paper focused on the building roof assessment of Emmanuel Alayande College of education Oyo. To achieve this aim, a UAV (Drone) was flied at a height to acquire the image data, which was processed using Argisoft Photoscan Professional image processing software and an orthomosaic, was acquire. The orthomosaic was analyzed with ArcGIS 10.3 and a map of building assessment of Campus was obtained. The findings shows geospatially how important is the geospatial technology for mapping roofs of infrastructure in this citadel of knowledge.

Parallel Processing Strategies for Geospatial Data in a Cloud Computing Infrastructure

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

Hybrid ResNet152-EML Model for Geo-Spatial Image Classification

Hybrid ResNet152-EML Model for Geo-Spatial Image Classification

Adversarial Learning Based Discriminative Domain Adaptation for Geospatial Image Analysis

The ability of supervised image analysis methods to provide state-of-the-art performance is limited by availability of high-quality labeled data in large quantities. Domain adaptation approaches propose a solution to this problem by leveraging quality labeled information from auxiliary data sources. In this work, we use adversarial learning for domain adaptation for remote sensing applications. First, we approached the problem of unavailable target domain labels with unsupervised domain adaptation and then extended our method for semisupervised domain adaptation to use a few available labels as well. We are using adversarial learning to extract discriminative target domain features that are aligned with source domain. We test our framework for two very different applications of remote sensing imagery, multiclass classification in hyperspectral images and semantic segmentation in large scale satellite images. For hyperspectral image analysis two datasets were used: the University of Houston shadow data was used for quantifying the efficacy of our approach to varying illumination, and the Botswana data was used to quantify the efficacy of our approach under multitemporal spectral shifts. Multisensor high-resolution images from National Agriculture Imagery Program and SpaceNet-Rio datasets were used as the source and target for the task of building extraction for large scale semantic segmentation based domain adaptation.

Delineation of field boundary from multispectral satellite images through U-Net segmentation and template matching

Geospatial images deliver a visual medium to recognize the prompt changes in the environment influenced due to seasonal changes. These changes extend their impact on the soil vegetation and crop growth patterns leading to an increase/decrease in agricultural production. Identification of seasonal changes and their impact using collected geospatial images within a specific time frame to predict land utilized for agriculture. Although identifying land cover utilization through spectral indices and bands are available, they generalize based on one particular region and may misclassify and overlap each other. The utilization of template matching enables in identifying the crop field area based on templates extracted and labelled. Masked images are acquired randomly from images or existing datasets labelled as fields cropped and barren. The specific region of interest is tiled and demarcated through Semi-supervised classification identifying the annual land crop cover. The extracted images are utilized as masked templates to identify the area of crop cover with an accuracy of 98%. The correlations of land cover helps in identify the specific Region of Interest (ROI) providing maximum and minimum utilization of cultivable lands.

The Russian Arctic World as a “cultural circle” and an object of development

The geospatial image of the Russian Arctic in the cultural and civilizational context is considered, taking into account the current policy and development strategy of the Arctic zone of the Russian Federation for the period up to 2035. Russian Arctic world as a domestic phenomenon, which embodies the specifics of Russian civilization, national culture, history and geography, is in the center of attention. It is emphasized that the “space factor” played and continues to play a huge role in the formation of Russian civilization. It left a vivid imprint on all aspects of public life. Overcoming the space in Russia still remains one of the most urgent state tasks. First of all, this applies to the Arctic zone of the Russian Federation, the strategic importance of which is becoming more and more significant. It is pointed out that the implementation of the unique geospatial capabilities of the Russian Arctic lies not only in the implementation of the strategic tasks of socio-economic development and technological advancement, but also in the spiritual, moral, cultural and professional growth of people living and working there.As the methodological basis of the study, the authors chose the concept of “spatial rotation”, which covers modern social theory and practice. Its main content boils down to the fact that space is recognized as an active participant in the interactions taking place on Earth. At the same time, the space is represented as a “characteristic” human work, saturated with the meanings of social dialectics and strategy. This statement was an argument to consider the fact of “spatialization” of the Russian Arctic into the category of a special life world. In this regard, the idea is expressed about the need for an intensive “humanization” of the Russian Arctic world.

Early Detection of Deforestation through Satellite Land Geospatial Images based on CNN Architecture

This study has developed a CNN model applied to classify the eight classes of land cover through satellite images. Early detection of deforestation has become one of the study’s objectives. Deforestation is the process of reducing natural forests for logging or converting forest land to non-forest land. The study considered two training models, a simple four hidden layer CNN compare with Alexnet architecture. The training variables such as input size, epoch, batch size, and learning rate were also investigated in this research. The Alexnet architecture produces validation accuracy over 100 epochs of 90.23% with a loss of 0.56. The best performance of the validation process with four hidden layers CNN got 95.2% accuracy and a loss of 0.17. This performance is achieved when the four hidden layer model is designed with an input size of 64 × 64, epoch 100, batch size 32, and learning rate of 0.001. It is expected that this land cover identification system can assist relevant authorities in the early detection of deforestation.

Topos of Egypt in Poetry of Silver Age

The specificity of the development of the geographical and cultural space of Egypt in the poetry of the Silver Age at the time when the “Russian” poetic Egypt was born as a system of leitmotifs, imagestopos and a specific lexicon is described. It is noted that in modern literary criticism, in comprehending the geopoetics of a regional text, works devoted to the European continent, in particular, geopoetic regional models of Russian literature, have been most fully investigated. The relevance of the study is seen in the need to comprehend and analyze the geopoetics of Egypt and, more broadly, Africa as a sacred geocultural space. The textual fragments of poetic works by K. Balmont, V. Bryusov, I. Bunin, N. Gumilyov, V. Khlebnikov, representing stable geospatial images and symbols of Egypt, are analyzed. The authors come to the conclusion that the poetry of the Silver Age combines geocultural images and symbols with mythological motives, which gives the topos of Egypt a geosophical meaning. The analyzed material made it possible to show the generalized artistic structure of the geopoetic representation of Egypt in the poetics of the Silver Age and to highlight the spatial geocultural dominants: the Nile, Africa, the desert, the Sphinx, Egyptian heroes as images-topos, the Arab East.