Landslides are a recurrent and damaging hazard in the mountainous terrain of Chin State, Myanmar, driven by steep slopes, fragile lithology, and intense monsoonal rainfall. This study develops a regional-scale landslide susceptibility mapping framework by integrating cloud-based geospatial processing in Google Earth Engine with the Analytical Hierarchy Process (AHP) for multi-criteria decision analysis. A landslide inventory was compiled through visual interpretation of high-resolution satellite imagery and used for model validation. Sixteen landslide-conditioning factors representing topography (slope, aspect, curvature), hydrology (drainage density, topographic wetness index, stream power index, distance to rivers), climate (mean annual precipitation), geology/structure (lithology, geomorphology, lineament density), land surface characteristics (land use/land cover, Normalized Difference Vegetation Index, Normalized Difference Water Index, soil texture), and anthropogenic influence (distance to roads) were derived from open datasets within Google Earth Engine (GEE). Each factor was standardized into five susceptibility classes and weighted using AHP; the consistency ratio (CR = 0.014) indicated acceptable pairwise judgments. The weighted linear combination produced a landslide susceptibility index (LSI), which was classified into five susceptibility zones. Validation using receiver operating characteristic (ROC) analysis on an independent sample of 183 points yielded an area under the curve (AUC) of 0.768, Accuracy = 0.699, F1-Score = 0.751, and Cohen's Kappa = 0.38, demonstrating acceptable predictive capability. High and very high-susceptibility areas mainly coincide with steep slopes, dissected terrain, dense drainage and lineament networks, weak lithologies, and proximity to roads and rivers. The resulting susceptibility map provides actionable information for land-use planning and risk reduction in data-scarce mountainous regions and can be readily transferred to other areas using the same GEE-based workflow.
Accurate lightning mapping is crucial for improving lightning characterisation studies in Indonesia. However, the limitations of existing technology and mapping methods hinder a deeper understanding of this phenomenon. This study presents the preliminary implementation of a very high frequency (VHF) interferometry (ITF) system at the Universitas Sriwijaya (UNSRI) Lightning Station, representing the first regional implementation of its kind in Indonesia. The interferometry system utilises three VHF antennas, designated as antennas B, C, and D, along with one fast field antenna, to capture radio frequency emissions from lightning events. The system is located at the Graha Bukit Asam, UNSRI, with the antennas arranged as two orthogonal 10 m baselines. A MATLAB-based processing chain computes azimuth and elevation using a cross-correlation technique by measuring the time differences between antennas B and C, and antennas B and D. The upsampled VHF signals were then segmented using a 131,072-sample window and a 1024-sample shift between consecutive windows. A total of eleven positive Narrow Bipolar Events (PNBEs) were observed between April and June 2024; of these, two events satisfied a 70% radiation-capture threshold and were successfully mapped in two-dimensional azimuth–elevation space, illustrating the spatiotemporal evolution of the PNBE radiation sources. The primary contribution of this study is a preliminary high-temporal-resolution two-dimensional azimuth–elevation reconstruction of two selected PNBE cases at an equatorial site (Palembang), providing a methodological foundation for further regional implementation. The mapped result presented here is preliminary and based on two PNBE cases; the site-specific calibration of the UNSRI station is currently in progress, and broader cross-validation against established lightning detection networks remains future work.
Riyadh, Saudi Arabia, is undergoing rapid urban transformation driven by large-scale infrastructure development projects. Recently, Riyadh Metro project is considered one of the cutting-edge technologies that aims to decrease the overwhelming road congestion. However, the general ground deformation caused by tunneling and excavation activities may cause a serious threat to the infrastructures. This study investigates ground deformation associated with the Riyadh Metro constructions over a six-years period (2017–2023) using advanced remote sensing techniques. Interferometric Synthetic Aperture Radar (InSAR) analysis was implemented through the Small Baseline Subset (SBAS) approach to derive high-resolution deformation measurements. A total of 252 Sentinel-1 SAR scenes from the ascending orbit were processed, generating 126 interferograms from the IW mode, with analysis focused on the F2 subswath covering the metropolitan area of Riyadh. Line-of-sight (LOS) velocity fields and deformation time-series were produced to quantify spatial and temporal patterns of ground movement. Validation was conducted using observations from the SAS0 GNSS station for the period (2019–2021) demonstrating strong agreement between satellite-derived and ground-based measurements. The results reveal localized subsidence exceeding 6 cm/year in major construction zones, particularly near metro line intersections, tunneling corridors, and deep excavation sites. Temporal analysis indicates a pronounced acceleration of subsidence in early 2020, interpreted as a delayed geotechnical response to intensive excavation and loading activities during 2019. These findings confirm the robustness and reliability of the SBAS-InSAR techniques for long-term urban deformation monitoring. Furthermore, the study underscore the critical importance of continuous geodetic surveillance in mitigating geotechnical risks, safeguarding adjacent infrastructures and supporting sustainable urban development during and after megaprojects such as the Riyadh Metro.
Accurately estimating orbital-debris lifetimes is essential for ensuring the long-term sustainability of the space environment. However, many fragments either decayed before systematic tracking began or remain unobserved today, resulting in incomplete lifetime data. This study develops a doubly censored Weibull framework to model debris persistence in low Earth orbit (LEO), accounting simultaneously for early untracked re-entries and still-orbiting objects. Parameters are estimated via maximum likelihood, with uncertainty assessed using bootstrap resampling. A one-stage multiple-comparison (MCC) procedure is introduced to test altitude-dependent differences in median lifetimes under heavy censoring. Applied to fragments from the 2009 Iridium–Cosmos collision, the results reveal strong altitude dependence: debris near 700 km exhibits lifetimes below eight years, while fragments above 825 km remain quasi-stable over 16 years. The Weibull model consistently outperforms Lognormal and Exponential alternatives, capturing the gradual, altitude-driven evolution of the re-entry hazard. By coupling survival-analysis methodology with orbital-mechanics interpretation, this framework provides a reproducible, physically interpretable, and policy-relevant foundation for estimating orbital-debris persistence and guiding mitigation strategies in upper LEO.
Accurate and location-specific data on land use and land cover (LULC) are essential for quantifying anthropogenic and climate-driven impacts in complex tropical environments. However, many existing LULC studies are constrained by coarse spatial resolution, limited spectral information, or inadequate integration of ancillary data such as topography factors that reduce classification accuracy in heterogeneous landscapes. This study evaluated the performance of two optical remote sensing datasets from Sentinel-2 and Landsat-8 for LULC mapping. Using imagery acquired between October to December of 2023, we tested three machine learning algorithms: Random Forest (RF), Classification and Regression Trees (CART), and Support Vector Machine (SVM) under three scenarios: (i) using only spectral bands (ii) spectral bands with additional bands, and (iii) spectral bands and topographic variables. Five major LULC types namely: water body, forest, low vegetation, built-up area, and bare land were identified and mapped. Scenario (iii) and RF classifier achieved the highest overall accuracy (89%) and a Cohen's kappa coefficient (0.86), while SVM performed least effectively (74% accuracy, kappa = 0.64). Although Landsat-8 imagery yielded slightly higher numerical accuracy, Sentinel-2 imagery resulted in finer spatial detail and more precise class delineation. These findings highlight the complementarity of both datasets and demonstrate that incorporating topographic predictors significantly enhances LULC classification performance. Overall, the study underscores the potential of freely available satellite data and machine learning integration for reliable, high-resolution LULC mapping in humid tropical regions by providing a foundation for improved environmental monitoring, resource planning, and climate adaptation strategies.
The Greater Cairo Metro has expanded substantially, yet the integration between transit infrastructures and surrounding land use remains uneven, constraining the delivery of sustainable Transit-Oriented Development (TOD). This study evaluates the TOD performance of 77 operational metro stations using a multi-dimensional spatial index derived from the 4Ds framework (Density, Diversity, Design, and Economic Development). Using GIS and Multi-Criteria Decision Analysis (MCDA), nine spatial variables were measured within station catchments, standardized, and aggregated to generate composite TOD scores for network-wide benchmarking. Results indicate that high TOD performance is spatially concentrated in the established urban core: only 12 stations (15%) achieve high composite scores, while peripheral stations lag considerably. The analysis also reveals a density-without-accessibility paradox. Although 65% of stations exhibit high residential density, validation against pedestrian Walkability Catchment Coverage shows only a weak association between TOD scores and effective walk-to-transit access (r = 0.25), implying that density and functional intensity do not reliably translate into pedestrian accessibility. Approximately 31% of the network functions as “disconnected nodes,” where high-density catchments are severed from stations by physical barriers and low street-network permeability. Building on these patterns, stations are classified into five intervention typologies, ranging from residential anchors requiring economic diversification to disconnected nodes requiring urgent morphological repair and first-mile retrofits. The findings demonstrate that uniform TOD policies are ill-suited to Greater Cairo's heterogeneous urban fabric and support a context-sensitive approach that prioritizes walkability, urban morphology, and targeted economic restructuring alongside densification.
The evolution of tidal flats is influenced by various forcing factors resulting from interactions among terrestrial, oceanic, and atmospheric environments. This study employs spatial analysis models to assess the potential for deposition of tidal flats along the northern coast of Vietnam. A series of 14 forcing factor maps, categorized into four groups sediment sources, oceanographic factors, coastal properties, and tidal flat properties, is derived. These forcing factors are analyzed using both heuristic and empirical approaches, and their relevance to deposition during the period 1989–2014 is evaluated using the Relative Operating Characteristic (ROC) method. The analysis quantifies the relative discriminatory influence of each factor on historical deposition. Subsequently, the Multi Criteria Evaluation (MCE) method is applied to integrate critical factors with varying weights, generating 9 scenarios of deposition potential. Scenario 5, produced using a weight series linearly proportional to the relative discriminatory influence of the forcing factors and showing the best fit to historical deposition, is selected to generate the potential map of future tidal flat deposition. This map achieves an accuracy of 77.6%, indicating high deposition potential around the Red River mouth and low potential along rocky coastal tidal flats. The study represents a significant advancement in applying complex spatial analysis methods to predict the potential for future changes of tidal flats within diverse coastal environments at a large scale.
Sediment plumes and associated levels of turbidity have immense significance for coastal water quality, health of ecosystems and biogeochemical processes in estuarine and near-shore systems. Remote sensing combined with geographic information systems and machine learning stands as an effective framework for monitoring such parameters on regional to national scales and over long-time spans, particularly applicable where coastal areas are sparsely covered by data. This study presents an RS-GIS-ML amalgamation approach to model sediment plume dynamics, turbidity, and surface water quality in coastal waters of Great Yarmouth, UK, using multi-temporal satellite data between 2020 and 2025. The objective of the study is to design remote sensing-based machine learning models for predicting water quality and sediment plume characteristics, and assess the accuracy and reliability of RS-derived water quality products with statistical performance assessment. Preprocess multi-source satellite data by extracting spectral bands and water quality sensitive indices. Machine learning models were applied to estimate turbidity, TDS, TOC, Chl-a, SSC, WQI, and sediment plume index. GIS techniques were involved in spatial analysis and plume delineation, and spatiotemporal trend assessment. Results of the model performance evaluation indicated an excellent prediction capability with R2 ranging from 0.81 to 0.89. The RMSE values were 6.82 NTU for turbidity, 78.45 mg/L for TDS, 0.62 mg/L for TOC, 1.15 μg/L for chlorophyll-a, 14.9 mg/L for SSC, 7.25 for WQI, and 0.18 for the sediment plume index based on 36–44 validation points. Overall, the findings offer validation for the robustness of the proposed framework for long-term coastal water quality monitoring.
Individual tree extraction is challenging from LiDAR point cloud data of artificial ginkgo forests with high canopy density. To solve this problem, this paper proposes an adaptive marker-controlled watershed algorithm based on Particle Swarm Optimization (PSO). The algorithm dynamically optimizes key parameters to reduce reliance on manual parameter tuning and achieve high-precision individual tree segmentation. Experimental results show that the matching accuracy of the proposed algorithm with field survey data is 78%, higher than 75% of the traditional algorithm. The inversion accuracy of tree height and crown width reaches 91.3% and 72%, respectively. The proposed algorithm effectively improves the application performance of LiDAR in high-density artificial forest land and provides reliable technical support for refined forest resource investigation.
Groundwater resources represent the primary freshwater source in arid regions such as north-central Sinai. This study aims to evaluate groundwater occurrence using an integrated approach that combines GIS-based multi-criteria analysis, gravity data interpretation, and vertical electrical sounding (VES). Key controlling factors, including rainfall, soil, land use/land cover, drainage density, slope, and lineament density, were integrated to delineate groundwater surface recharge potential. The analysis classified the study area into five zones, with moderate to good potential areas showing spatial agreement with VESs locations.Gravity data filtering using various techniques revealed faults trending mainly NE-SW, WNW-ESE, ENE-WSW, E-W, N-S, and NNE-SSW, along with minor NW-SE and NNW-SSE trends. These faults may act as conduits or barriers to groundwater flow. Moreover, 2-D gravity data modeling estimated basement depths of 1.7 to 5.4 km, which reveals a thick sedimentary section that contains groundwater aquifers. Quantitative interpretation of 50 VESs identified a four-layer geoelectrical sequence, with the deepest layer composed of fractured limestone, representing the shallow aquifer in the study area at depths of 311–400 m with resistivity values ranging from 40.8 to 153 Ω.m. Lateral variations in the overlying marl and limestone layers were linked to shallow faults that match the major structural trends delineated using gravity data.Overall, this study demonstrates that integrating GIS-based multi-criteria analysis with the interpretation results of gravity and geoelectrical data provides a robust framework for identifying promising groundwater zones and delineating groundwater potential in arid and structurally complex regions with scarce well data, such as north-central Sinai.
Remote sensing has become an essential tool in geological investigations, particularly for lithological mapping and discrimination of rock units. It is especially effective for studying inaccessible terrains and for acquiring detailed geological information over large areas. In this study, Landsat 8 Operational Land Imager (OLI) multispectral data were employed to extract lithological and hydrothermal alteration information using a set of advanced image-processing techniques. These include band ratios (BR), principal component analysis (PCA), correlation coefficient (CC), and optimal index factor (OIF). False color composites using bands 7, 5, and 1, selected band ratios (4/2, 5/6, and 6/7), and principal component images (PC5, PC2, and PC1) were applied to enhance and analyze the spectral characteristics of the lithological units in the study area. Furthermore, hydrothermal alteration indices were used to delineate zones enriched in metallic elements. The remote sensing results were integrated with geological field observations and analyzed within a Geographic Information System (GIS) environment to perform a multi-criteria characterization of mineralized areas. The findings demonstrate that remote sensing techniques significantly improve and update existing geological maps of the region. Key geological contacts, as well as mineralized dykes and veins of economic interest, were successfully identified using Landsat 8 OLI multispectral imagery. Field verification and geochemical sampling carried out by the Ministry of ONHYM during cartographic missions confirmed the accuracy and reliability of the remote sensing interpretations.
Surface displacement in rapidly urbanizing deltaic environments represents a growing concern due to its potential impacts on infrastructure and land sustainability. This study presents a DInSAR-based assessment of relative surface displacement in Egypt's East Delta using two Sentinel-1 SAR acquisitions from 2018 and 2024, integrated with multi-temporal land use/land cover (LULC) analysis. The study estimates cumulative LOS displacement between two acquisition dates, not continuous deformation rates. Differential Interferometric Synthetic Aperture Radar (DInSAR) processing was applied to estimate cumulative line-of-sight displacement patterns, while supervised classification of optical satellite imagery was used to map LULC changes over the same period. Statistical analysis was conducted to examine the spatial relationship between displacement patterns and dominant land use classes. The results represent indicative spatial patterns of surface instability.The results reveal spatially variable displacement signals that show noTable association with urban expansion and agricultural land conversion. While the estimated displacement magnitudes are indicative and subject to atmospheric and decorrelation effects, the integrated analysis highlights areas potentially vulnerable to surface instability. The study demonstrates the value of combining DInSAR-derived displacement patterns with LULC information for preliminary surface stability assessment in data-scarce deltaic regions. The analysis is not intended to provide geodetically validated subsidence measurements.
Open-ocean mariculture is expanding rapidly, yet comprehensive monitoring remains limited because most national-scale approaches rely on optical imagery or optical-SAR fusion, both of which are vulnerable to cloud cover and atmospheric conditions. Although SAR provides all-weather observations and Sentinel-1 enables costeffective national coverage, conventional backscatter-driven processing fails in low-contrast, speckle-dominated open-sea environments. Consequently, a Sentinel-1-only nationwide solution is still lacking. Here, we propose a phase-domain paradigm that operates in an observational space fundamentally different from intensity-driven methods. A locally weighted phase-averaging model first enhances structural coherence under dynamic ocean conditions, and multi-scale intra- and inter-block phase features are then extracted to robustly identify raft and cage aquaculture. Applied across all Chinese waters, the method yields 94.13% accuracy (F1 = 0.914) and produces the first SAR-only nationwide map of open-ocean mariculture (378,403 ha), revealing a pronounced north-south contrast and large-scale spatial clustering. Fully automated without labeled training data, the framework provides a scalable, cloud-independent monitoring route that can support long-term offshore aquaculture governance and marine spatial planning beyond China, subject to regional validation.
Urban expansion is frequently accompanied by the proliferation of informal or non-regulatory constructions, which pose major challenges to sustainable land management. Remote sensing combined with machine learning (ML) provides a robust framework for monitoring such dynamics over space and time. This study investigates the spatio-temporal evolution of non-regulatory habitats in Bni Makada, Tangier (Morocco), using Sentinel-2 imagery (2017 and 2025), machine learning classifiers (ANN, SVM, RF), and ancillary datasets including the Microsoft Building Footprints and the official urban development plan. After spectral band selection and supervised classification, model performance was assessed using recall, precision, F1-score, overall accuracy, and Cohen's kappa index. Results show that the ANN classifier outperformed other models (OA = 0.93; kappa = 0.87), enabling reliable discrimination between built-up and non-built-up areas. Between 2017 and 2025, Bni Makada recorded a net increase of 0.68 km2 in built-up surface, with more than 10,000 constructions identified in nonregulatory zones. Although the relative share of informal units decreased slightly (from 39.6% to 37.2%), their absolute number rose (+268 constructions), reflecting persistent urban governance challenges. The integration of satellite imagery, ML, and planning data provides a transferable methodology for detecting, quantifying, and managing non-regulatory constructions, thereby offering actionable insights for urban planning authorities in Morocco and comparable contexts worldwide.
Accurate and temporally consistent land use and land cover (LULC) mapping is important for environmental monitoring, ecosystem evaluation, and sustainable land management, especially in mountainous areas with intricate topology and spectral heterogeneity. Nonetheless, most existing machine learning and deep learning approaches have limited spectral interpretability and temporal instability, which undermine multi-temporal change detection. This research study proposes a feature-aware spectral learning framework of pixel-wise multi-temporal LULC classification based on Sentinel-2 surface reflectance data. In a common experimental environment, four pixel-wise spectral learning models, including Feature-Aware Convolutional Neural Network (FA-CNN), Feature-Aware Kolmogorov-Arnold Network (FA-KAN), Feature-Aware Extended Vision Transformer (FA-ExViT), and the proposed Feature-Aware Spectral Vision Transformer (FS-ViT), are comparatively evaluated. While FA-CNN learns local spectral responses, FA-KAN learns nonlinear feature interactions, and FA-ExViT learns global spectral dependencies with self-attention, whereas FS-ViT learns feature-aware spectral tokenization, adaptive spectral reweighting, and coarse-to-fine hierarchical supervision to increase class separability and temporal consistency. The framework is used for multi-temporal LULC mapping 2019, 2021, 2023, and 2025 of the mountainous Coonoor regions. Findings indicate that FS-ViT is consistently superior to comparison models, achieving overall accuracies between 96.46% and 98.07% having Kappa coefficients above 0.93. Explainable artificial intelligence (XAI) exhibits the physically significant and temporally consistent spectral feature contributions. Multi-temporal change analysis shows a net forest loss of 6.31 km2, primarily transitioning to low vegetation, alongside vegetation recovery and limited urban expansion. Overall, the proposed FS-ViT framework offers a decipherable and time-resilient solution for long-term LULC mapping and change detection in environmentally sensitive mountainous landscapes.
Remote sensing techniques have been widely used to monitor chlorophyll-a (Chl-a) concentrations in coastal waters. However, the accuracy and transferability of retrieval algorithms across different coastal environments remain uncertain. In this study, we evaluated the performance of a Chl-a retrieval algorithm originally developed for other coastal systems and recalibrated it using high spatial resolution (10 m) Sentinel-2 A (S2A) imagery. The MR4B algorithm employs blue, green, red, and near-infrared (NIR) bands for Chl-a estimation. Validation was conducted against in situ measurements collected on August 21, 2021, July 27, 2022, March 19, 2023, and October 10, 2023, coinciding with S2A overpass times. In addition, a new empirical model, based on green-blue and green-red band ratios, was developed. The MR4B algorithm performed well during the August and July campaigns but exhibited substantial errors for the March and October datasets, with RMSE (MAPE) values of 2.47 & micro;g/L (23.52%), 2.72 & micro;g/L (29.65%), 6.15 & micro;g/L (140%), and 7.23 & micro;g/L (256%), respectively. In contrast, the green-blue band-ratio algorithm produced more stable estimates of mean Chl-a concentrations, with RMSE (MAPE) values of 2.55 & micro;g/L (26.69%), 4.04 & micro;g/L (51%), 4.37 & micro;g/L (64%), and 5.95 & micro;g/L (200%) for the corresponding dates. Overall, the results demonstrate that the S2A green-blue ratio algorithm provides a more robust and operationally practical approach for Chl-a monitoring in this coastal system, owing to its simpler formulation and more consistent performance relative to the MR4B algorithm.
The interpretation of multi-modal remote sensing (RS) images is of great significance in the field of Earth observation. However, there are still challenges in the research process, such as feature redundancy, high computational overhead and suboptimal fusion strategy design. This paper presents a novel joint network based on dynamic Mamba and attention graph convolution for multimodal remote sensing image classification. This method is mainly composed of the Spa-Spe Mamba module, the Fu-Spa-Spe Mamba module and the Fu-GCN module. Specifically, in the feature extraction process, dynamic Spa-Spe Mamba blocks were introduced, which can reduce the redundant information of features in multi-path scanning, thereby improving the computational efficiency. In feature fusion, the introduction of Fu-Spa-Spe Mamba block can reduce the difference between heterogeneous features. The introduction of Fu-GCN can construct a spatial association structure between the fused features, and the self-attention mechanism calculates the correlation of the fused features, ultimately obtaining high-quality fused feature information. Finally, to verify the effectiveness of the method proposed in this paper, this paper selects two multimodal remote sensing datasets for relevant comparative experiments. The experimental results prove that this method has better classification performance.
Mangrove ecosystems face growing threats from human activity and erosion. This study aims to map mangrove distribution and canopy density and to evaluate the relationship between Normalized Difference Vegetation Index (NDVI) and field-measured canopy cover in East Belitung, Indonesia, by integrating high-resolution Pleiades imagery, NDVI, and hemispherical photography. A total of 50 field plots were used to assess canopy cover through hemispherical photography, while Object-Based Image Analysis (OBIA) with Support Vector Machine (SVM) classification was applied to map mangrove distribution and density. Results show that very dense canopies were largely associated with stands dominated by Rhizophora spp. along riverbanks and estuaries, especially in the northern and southern parts of the study area, with favorable hydrological conditions. Classification accuracy reached 83.3% (Kappa = 0.71), and canopy cover was strongly correlated with NDVI (r = 0.855), validating the method. The regression analysis further showed that canopy cover explained a substantial proportion of NDVI variation (R2 = 0.731). Combining satellite data with field validation offers a scalable framework for mangrove monitoring and supports coastal resilience and sustainable management.
Magnesite is an economically important industrial mineral widely used in refractory, agricultural, and chemical industries. In South Africa, magnesite deposits are limited, necessitating efficient exploration techniques to identify new and underexplored resources. This study investigates the occurrence and spatial distribution of magnesite in the Tshipise magnesite field, Limpopo Province, South Africa, using remote sensing techniques integrated with field verification. Landsat 8 OLI imagery was processed using True Colour Composite (TCC), False Colour Composite (FCC), band ratioing, and Principal Component Analysis (PCA) to enhance geological structures and magnesite-related alteration zones. The results demonstrate that magnesite mineralisation is structurally controlled and spatially associated with regional and local fault systems. High lineament density zones showed strong spatial correlation with known magnesite occurrences. Band ratios (4/2, 6/7, and 6/5) and PCA (2, 5, 6, 7) effectively highlighted hydrothermal alteration and carbonate mineral signatures. Field verification confirmed magnesite occurrences within ultramafic host rocks, dolerite dykes, and vein systems. This study confirms that integrated remote sensing techniques provide a rapid, cost-effective, and reliable approach for mapping magnesite mineralisation in both greenfield and brownfield exploration environments.
