Orthophoto Map Research Articles

Potential formational scenarios of the mud volcanoes in the Zhurong landing area in Utopia Planitia, observed by Tianwen-1

The widely distributed pitted cones in Utopia Planitia have several explanations, and they have been interpreted as mud volcanoes in the Zhurong landing area. The morphology and distribution characteristics of mud volcano cones can serve as indicators of stress direction and can also be used to calculate potential depth for mud sources. This information may assist in elucidating the potential formational scenarios of the mud volcanoes, which is of great significance to study the tectonic evolution in Utopia Planitia and possible water ice depths. The digital orthophoto map (DOM) and the digital elevation model (DEM) with spatial resolutions of 0.7 m and 3 m obtained by High Resolution Imaging Camera (HiRIC) from Tianwen-1 show that the mud volcanoes are distributed as both isolated cones and clustered cones in the Zhurong landing area. The age of the mud volcanoes is limited to the middle to late Amazonian (∼2.0 Ga - 400 Ma) based on the crater size-frequency distribution (CSFD); the depth range of the mud source is estimated to be ∼0.6–7.2 km by self-similar clustering analysis. The orientations of the aligned cones and the elongated mud vents indicate that the maximum horizontal stress (SH) directions during the formation of the mud volcanoes in the landing area are in the direction of N67°E, which is in good agreement with the orientations of the nearby troughs, suggesting that the formation of the mud volcanoes in the landing area may have been controlled by the fracturing of the lower part of the troughs. These features, compared with those reported in previous study of mud volcanism/cone morphology on Mars, collectively suggest that the mud volcanoes in the landing area may have formed in a geological scenario under conditions of volcanic destabilization. Further, the formation of the mud volcanoes, with the analysis of the SH, may have been influenced by both basin subsidence and the volcanism in the Elysium region. This study provides a new example of Martian mud volcano geologic formation scenarios, offers the feasibility of using the SH to analyze Martian mud volcano formation, and hints at active geologic phenomena on Mars during the Amazonian period.

A fully automated model for land use classification from historical maps using machine learning

Digital land use data before the age of satellites is scarce. Here, we build a machine learning model, using Extreme Gradient Boosting, that can automatically detect land use classes from an orthophoto map of Sweden (economic maps, 1:10 000 and 1:20 000) constructed from 1942 to 1988. Overall, the machine learning model demonstrated robust performance, with Cohen's Kappa and Matthews Correlation Coefficient of 0.86. The F1 values of the individual classes were 0.98, 0.95, 0.84, and 0.87 for graphics, arable land, forest, and open land, respectively. While the model can be used to detect land use changes in arable land, higher uncertainties associated with forest and open land necessitate further investigation at regional scales or exploration of improved mapping techniques. The code is publicly available to enable easy adaptation for classifying other historical maps.

DRA-UNet for Coal Mining Ground Surface Crack Delineation with UAV High-Resolution Images.

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation.

Assessing the relationship between urban park spatial features and physical activity levels in Residents: A spatial analysis Utilizing drone remote sensing

The park environment is crucial for promoting physical activity (PA). While numerous studies show that park environments influence PA behavior, inconsistencies remain, likely due to varing research methods and parks types. This study employs a fixed spatial grid method to systematically sample four representative parks in Tianjin, China. High-precision orthophoto map (DOM) data from drones provided detailed environmental attributes (like tree canopy area, lawn area, and paved area) and PA characteristics (number of participants, intensity, diversity). The results show: 1) Cluster analysis grouped 1839 park grids into 12 environmental attribute integrations, each correlating with different PA characteristics. “Tree-lined jogging corridors” and “Large sports field areas” exhibit the highest PA intensity, while “Entrance plazas”, “Central plazas,” and “Open sports spaces” have the highest number of participants and PA diversity. 2) Correlation analysis shows that various environmental attributes, including Lawn Area, and Paved Area, are significantly correlated with PA characteristics. 3）Random Forest analysis indicates the key attributes are the paved area for the number of PA participants and PA diversity, and specialized sports facilities area for PA intensity. These findings support urban green space planning and highlight the importance of better park environments for public health.

Tourism and Urban Development: The case of a coastal village in a small island state

This paper discusses how Marsascala, a coastal town in Malta, has been urbanized over the years, with particular attention to the development of the tourist industry. Young’s (1983) “general model of the process of ‘touristization’ and landscape change” is engaged with to study the impact of tourism development on the locality and local community. Different research methods were used, including qualitative interviews via thematic analysis, fieldwork, orthophoto maps, and analysis of secondary data. Findings show that tourism plays a major role in the local economy. Overdevelopment and population increase (fuelled by tourism and influx of foreign workers) are major challenges in the locality. The former fishing village has as a result reached stage six (intensive tourism consolidation) of Young's model. While more services are available to locals and visitors, the quality of life in the locality is deteriorating. This has raised questions about the need to redevelop the Jerma Hotel which will partly be a real estate project. The authors propose a seventh stage to Young’s model - ‘real estatation’ whereby more areas are taken over by real estate projects including for short-term rentals. This paper is linked to the EU Cost Action CA221222 Rethinking the Blue Economy: Socio-Ecological Impacts and Opportunities (RethinkBlue), in particular in relation to the themes covered by Working Group 3 - Port cities & coastal communities.

Seasonal cap dynamics at the South Pole of Mars in the 36th Martian year, observed by Tianwen-1

The seasonal variation of the Martian South Polar Seasonal Cap (SPSC) is a significant factor to influence Mars atmospheric cycle, which has been continuously observed in previous missions. The SPSC degrades asymmetrically throughout the Martian seasons. However, developing a general description for this asymmetric recession is challenging, and this is crucial for identifying key stages (timesteps) which represents moments of special attribute throughout the recession. Here, we present the Martian Year 36 (MY36) results from the continuous observation of the Martian SPSC by the Moderate Resolution Imaging Camera (MoRIC) onboard the Tianwen-1 orbiter. A time-series of digital orthophoto maps (DOMs) characterizing the south cap were collected ranging from the solar longitude (Ls) 210° to 332°. A seasonal recession model of the cap was established by incorporating a 5th-order polynomial fitting of the time-series on cap latitude, from which the first derivative (recession rate), second derivative (acceleration), and third derivative (jerk) can be obtained. This methodology enables the extraction of critical timesteps that facilitate the categorization of the recession into distinct phases, each characterized by its recession rate and other factors. By the visual interpretation of remote sensing images obtained by Tianwen-1 and the result of timesteps showed that the pause time of the South Polar Residual Cap (SPRC) is about Ls = 324.5°-328° with an area of 7.9 × 104 km2. The discrepancies between our results and previous investigations indicate that the Martian climate presents inter-annual fluctuations. The MoRIC image data can be used to quantitatively capture the fine recession dynamic of the southern polar cap.The continuous operation of the Tianwen-1 orbiter will enable further data analysis for the dynamics of the Martian polar caps. This will provide observational constraints for Martian climate models and crucial information for the research of the Martian modern climate.

Pseudo-Spectral Spatial Feature Extraction and Enhanced Fusion Image for Efficient Meter-Sized Lunar Impact Crater Automatic Detection in Digital Orthophoto Map.

Impact craters are crucial for our understanding of planetary resources, geological ages, and the history of evolution. We designed a novel pseudo-spectral spatial feature extraction and enhanced fusion (PSEF) method with the YOLO network to address the problems encountered during the detection of the numerous and densely distributed meter-sized impact craters on the lunar surface. The illumination incidence edge features, isotropic edge features, and eigen frequency features are extracted by Sobel filtering, LoG filtering, and frequency domain bandpass filtering, respectively. Then, the PSEF images are created by pseudo-spectral spatial techniques to preserve additional details from the original DOM data. Moreover, we conducted experiments using the DES method to optimize the post-processing parameters of the models, thereby determining the parameter ranges for practical deployment. Compared with the Basal model, the PSEF model exhibited superior performance, as indicated by multiple measurement metrics, including the precision, recall, F1-score, mAP, and robustness, etc. Additionally, a statistical analysis of the error metrics of the predicted bounding boxes shows that the PSEF model performance is excellent in predicting the size, shape, and location of impact craters. These advancements offer a more accurate and consistent method to detect the meter-sized craters on planetary surfaces, providing crucial support for the exploration and study of celestial bodies in our solar system.

Establishment of orthophoto map using Lidar technology combined with digital photography at Tan Son Nhat Airport, Ho Chi Minh City

Establishment of orthophoto map using Lidar technology combined with digital photography at Tan Son Nhat Airport, Ho Chi Minh City

Application of Orthophoto Maps Created from UAV Aerial Images for Monitoring Historical and Cultural Heritage Lands

The study investigates the methodology of creating orthophotoplans for immovable historical and cultural heritage sites, beginning from aerial surveys using various UAVs to object vectorization and plan creation. This issue is highly relevant in Ukraine due to rapid reforms in all ministries, including the Ministry of Culture. The reforms are creating a constant need for updated graphical documentation. As immovable historical and cultural heritage sites are in tourist areas with changing infrastructure, traditional surveying methods are impractical due to time constraints. Additionally, the application of UAV aerial imaging for delineating object boundaries and protective zones is more cost-effective. Existing boundaries of heritage lands and those determined from archival materials were overlaid on the created pflans. Orthophotoplans were produced and vectorized at a scale of 1:500 for two research zones: the Holy Trinity Church in Liuboml and the New Jewish Cemetery in Lviv. For the New Jewish Cemetery, boundaries were delineated to assess alignment with the current protective zone. Protective buffer zones around heritage sites were outlined according to both historical and contemporary recommendations in Ukraine. For buffer zones measured from the centroid, development is permitted up to 300 meters for undeveloped areas, with negotiated distances for developed regions, using the 300-meter protective zone as a baseline. The study's uniqueness lies in using a known methodology for creating orthophotoplans based on UAV data to explore options for modeling protective zones based on varying legislative norms over time, incorporating specific nuances. This approach can benefit not only Ukraine but also other countries undergoing reforms in this field.

Urban Transformation of the Dubrovnik Summer Villa Setting—From an Idyllic Landscape to an Overbuilt City

The landscape surrounding a summer villa is, in a morphological and experiential sense, an inseparable part of the villa and its garden. This research examines the level of preservation of the historic landscapes of eight summer villas in Dubrovnik and the level of their integration into the urban fabric of the contemporary city. The research methods used include the analysis of the relevant scientific literature as well as the analysis and interpretation of graphic materials, i.e., cadastral maps from the second half of the 19th century, orthophoto maps, and spatial planning documentation. In this paper, the immediate landscape in close vicinity to the summer villas is defined as the protective zone of a summer villa (PZSV). Although the original use and extent of the summer villa landscapes have rarely been preserved, it is possible to establish PZSVs in order to preserve, i.e., reinterpret, the setting of the summer villas, which is necessary to experience them in their totality. This research establishes the general principles that enable the identification of three general models of transformation of the summer villa landscape. Furthermore, this research develops possible scenarios for the enhancement of the summer villa landscape based on the identified models. These models and scenarios are generally applicable in the context of the protection and enhancement of the summer villa landscapes in of the south of Croatia.

The Generation of High-Resolution Mapping Products for the Lunar South Pole Using Photogrammetry and Photoclinometry

High-resolution and high-accuracy mapping products of the Lunar South Pole (LSP) will play a vital role in future lunar exploration missions. Existing lunar global mapping products cannot meet the needs of engineering tasks, such as landing site selection and rover trajectory planning, at the LSP. The Lunar Reconnaissance Orbiter (LRO)’s narrow-angle camera (NAC) can acquire submeter images and has returned a large amount of data covering the LSP. In this study, we combine stereo-photogrammetry and photoclinometry to generate high-resolution digital orthophoto maps (DOMs) and digital elevation models (DEMs) using LRO NAC images for a candidate landing site at the LSP. The special illumination and landscape characteristics of the LSP make the derivation of high-accuracy mapping products from orbiter images extremely difficult. We proposed an easy-to-implement shadow recognition and contrast stretching method based on the histograms of the LRO NAC images, which is beneficial for photogrammetric and photoclinometry processing. In order to automatically generate tie points, we designed an image matching method considering LRO NAC images’ features of long strips and large data volumes. The terrain and smoothness constraints were introduced into the cost function of photoclinometry adjustment, excluding pixels in shadow areas. We used 61 LRO NAC images to generate mapping products covering an area of 400 km2. The spatial resolution of the generated DOMs was 1 m/pixel, and the grid spacing of the derived DEMs was 1 m (close to the spatial resolution of the original images). The generated DOMs achieved a relative accuracy of better than 1 pixel. The geometric accuracy of the DEM derived from photoclinometry was consistent with the lunar orbiter laser altimeter (LOLA) DEM with a root mean square error of 0.97 m and an average error of 0.17 m.

Forest canopy height modelling based on photogrammetric data and machine learning methods

AbstractForest topographic survey is a problem that photogrammetry has not solved for a long time. Forest canopy height is a crucial forest biophysical parameter which is used to derive essential information about forest ecosystems. In order to construct a canopy height model in forest areas, this study extracts spectral feature factors from digital orthophoto map and geometric feature factors from digital surface model, which are generated through aerial photogrammetry and LiDAR (light detection and ranging). The maximum information coefficient, Pearson, Kendall, Spearman correlation coefficients, and a new proposed index of relative importance are employed to assess the correlation between each feature factor and forest vertical heights. Gradient boosting decision tree regression is introduced and utilised to construct a canopy height model, which enables the prediction of unknown canopy height in forest areas. Two additional machine learning techniques, namely random forest regression and support vector machine regression, are employed to construct canopy height model for comparative analysis. The data sets from two study areas have been processed for model training and prediction, yielding encouraging experimental results that demonstrate the potential of canopy height model to achieve prediction accuracies of 0.3 m in forested areas with 50% vegetation coverage and 0.8 m in areas with 99% vegetation coverage, even when only a mere 10% of the available data sets are selected as model training data. The above approaches present techniques for modelling canopy height in forested areas with varying conditions, which have been shown to be both feasible and reliable.

A Catalogue of Impact Craters and Surface Age Analysis in the Chang’e-6 Landing Area

Chang’e-6 (CE-6) is the first sample-return mission from the lunar farside and will be launched in May of 2024. The landing area is in the south of the Apollo basin inside the South Pole Aitken basin. Statistics and analyses of impact craters in the landing area are essential to support safe landing and geologic studies. In particular, the crater size–frequency distribution information of the landing area is critical to understanding the provenance of the CE-6 lunar samples to be returned and can be used to verify and refine the lunar chronology model by combining with the radioisotope ages of the relevant samples. In this research, a digital orthophoto map (DOM) mosaic with resolution of 3 m/pixel of the CE-6 landing area was generated from the 743 Narrow Angle Camera of the Lunar Reconnaissance Orbiter Camera. Based on the DOM, craters were extracted by an automated method and checked manually. A total of 770,731 craters were extracted in the whole area of 246 km × 135 km, 511,484 craters of which were within the mare area. Systematic analyses of the crater distribution, completeness, spatial density, and depth-to-diameter ratio were conducted. Geologic model age estimation was carried out in the mare area that was divided into three geologic units according to the TiO2 abundance. The result showed that the east part of the mare had the oldest model age of μ3.27−0.045+0.036 Ga, and the middle part of the mare had the youngest model age of μ2.49−0.073+0.072 Ga. The crater catalogue and the surface model age analysis results were used to support topographic and geologic analyses of the pre-selected landing area of the CE-6 mission before the launch and will contribute to further scientific researches after the lunar samples are returned to Earth.

A Case Study of the Integration of Ground-Based and Drone-Based Ground-Penetrating Radar (GPR) for an Archaeological Survey in Hulata (Israel): Advancements, Challenges, and Applications

This study delves into the fusion of ground-based and drone-based ground-penetrating radar (GPR) technologies in archaeological exploration. Set against the backdrop of the Hulata solar panel construction site in Israel, the research confronts daunting obstacles such as clayey soil, accurate detection of small objects, and the imperative of timely reporting crucial for construction management. The drone-based GPR, a testament to technological innovation, showcases remarkable adaptability to challenging terrains, dispelling doubts about electromagnetic wave decay in clayey soil. Methodologically, the study employs detailed orthophoto mapping and grid-type surveys. The correlation of the results significantly bolsters the reliability of archaeological discoveries, uncovering scattered artifacts buried approximately 1–1.5 m below the surface. Meticulous excavations validate the geophysical surveys, affirming the presence of structures constructed from boulders. The application at the Hulata site validates the adaptability of drone-based GPR in challenging terrains. It provides a swift, cost-effective, and minimally invasive alternative to traditional excavation techniques, thereby transforming the field of archaeology.

Georeferencing of Satellite Images with Geocoded Image Features

Abstract. Currently, using digital orthophoto map (DOM) and digital elevation model (DEM) as reference to achieve geometric positioning of newly acquired satellite images has become a popular photogrammetric approach. However, this method relies on DOM and DEM data which requires a lot of storage space in practical applications. In addition, for geometric positioning of satellite images, only sparse image feature points are needed as control points. Consequently, for the sake of convenience, the compression of control data emerges as a necessity with significant practical implications. This paper investigates a "cloud control" photogrammetry method based on geocoded image features. The method extracts SIFT feature points from DOMs, and obtains their ground coordinates, then constructs geocoded image feature library instead of DOM and DEM data as control, thus realizing the compression of control data. Experiments conducted on the Tianhui-1, Ziyuan-3 and Gaofen-2 satellite images demonstrate that the proposed method can achieve high-precision geometric positioning of satellite images and greatly reduce the size of the control data. Specifically, with the reduction of the reference data from 180~1248 MB 2 m DOM and 30 m DEM to 5~10 MB geocoded image features, the geopositional accuracies of the test Tianhui-1, Ziyuan-3 and Gaofen-2 images are improved from 3.12 pixels to 1.74 pixels, 3.69 pixels to 1.09 pixels, and 150.93 pixels to 2.67 pixels, respectively.

Calibrating an airborne linear-array multi-camera system on the master focal plane with existing bundled images

ABSTRACT Integrating multiple hyperspectral linear-array cameras into a Linear-array Multi-camera System (LMS) enables an expanded Field of View (FOV) while preserving high spatial resolution. However, the constrained capabilities of tie points are weakened by the limited overlap and short baseline between sub-cameras, hindering the attainment of reliable solutions for the theoretically ideal relative calibration model. Additionally, the absence of stereoscopic imaging in the LMS introduces the challenge of acquiring dense Ground Control Points (GCPs) throughout the FOV during calibration. In this paper, for calibrating an advanced airborne hyperspectral LMS named Arial Hyperspectral System-1000 (AHS-1000), we propose an equivalent geometric model that transforms the three-camera configuration of the LMS into a single-camera representation on the master camera’s focal plane, which was successfully applied to the data processing system of AHS-1000. We extracted dense GCPs by matching each Level-1 sub-image of the LMS with the bundled images of an existing three-line-scanner aerial image dataset, whose orientation parameters have been accurately solved by bundle adjustment with sparse GCPs; the matched points of each sub-image were consistently projected onto the master focal plane as image observations for our equivalent model. Finally, we established a collinearity equation system and performed a combined strip adjustment to calibrate the distortion terms for each pixel of the equivalent camera. The results demonstrate the effectiveness and robustness of our equivalent modeling method, which outperforms the classic modeling method by 2.83 pixels in interior accuracy and 0.86 pixels in relative accuracy, as well as outperforming another similar equivalent modeling method by 1.22 pixels in interior accuracy and 0.18 pixels in relative accuracy. Additionally, our comparison utilizing bundled images, public DOM (Digital Orthophoto Maps) /DEM (Digital Elevation Models), and high-precision DOM/DEM from aerial three-line-scanner demonstrates that bundled images can achieve higher calibration accuracy.

Geomorphological Response of Alluvial Streams to Flood Events during Base-Level Lowering: Insights from Drone-Based Photogrammetric Surveys in Dead Sea Tributaries

The geomorphological impact of base-level lowering on ephemeral alluvial streams has been extensively investigated through fieldwork, experimentation, and modeling. Yet, the understanding of hydrological parameters governing the dynamics of the stream’s geometry during discrete flood events is lacking due to limited direct measurements of flood-scale erosion/deposition. The emergence of novel remote sensing methods allows for quantifying morphological modifications caused by floods in alluvial streams. This study utilizes drone surveys and hydrological data to quantitatively investigate the relation between channel evolution in alluvial tributaries draining to the receding Dead Sea and the hydrological characteristics of flash floods. Drone-based photogrammetric surveys were conducted before and after 25 floods, over a period of four years, to generate centimeter-scale Digital Elevation Models (DEM) and orthophoto maps of two major streams. The outcomes of these DEMs are maps of ground elevation changes (erosion/deposition), thalweg longitudinal profiles, and channel cross sections, revealing the incision/aggradation along and across the streams. Statistical comparison of results with flow hydrographs identified potential relations linking the hydrological characteristics of each flood and the corresponding geomorphological modifications. Peak discharge emerged as the primary factor influencing sediment removal, leading to more efficient sediment evacuation and a negative sediment budget with increased discharge. Water volumes of floods also exhibited a secondary effect on the sediment budget. The chronological order of floods, whether first or later in the season, was identified as the primary factor determining incision magnitude. Knickpoints formed at the streams’ outlets during the dry period, when lake-level drops, amplifying the impact of the first flood. These findings have potential implications for infrastructure planning and environmental management in the context of climate change and altered water runoff. The research highlights the efficiency of drone-based photogrammetry for cost-effective and timely data collection, providing invaluable flexibility for field research.

Metric localization for lunar rovers via cross-view image matching

Accurate localization is critical for lunar rovers exploring lunar terrain features. Traditionally, lunar rover localization relies on sensor data from odometers, inertial measurement units and stereo cameras. However, localization errors accumulate over long traverses, limiting the rover’s localization accuracy. This paper presents a metric localization framework based on cross-view images (ground view from a rover and air view from an orbiter) to eliminate accumulated localization errors. First, we employ perspective projection to reduce the geometric differences in cross-view images. Then, we propose an image-based metric localization network to extract image features and generate a location heatmap. This heatmap serves as the basis for accurate estimation of query locations. We also create the first large-area lunar cross-view image (Lunar-CV) dataset to evaluate the localization performance. This dataset consists of 30 digital orthophoto maps (DOMs) with a resolution of 7 m/pix, collected by the Chang’e-2 lunar orbiter, along with 8100 simulated rover panoramas. Experimental results on the Lunar-CV dataset demonstrate the superior performance of our proposed framework. Compared to the second best method, our method significantly reduces the average localization error by 26% and the median localization error by 22%.

AN EXPLORATORY STUDY OF THE EVOLUTION OF URBAN GREEN SPACES IN LISBON USING DIACHRONIC ANALYSIS OF ORTHOPHOTO MAPS

Green urbanism, namely in the form of consolidated urban green spaces (UGSs), has gained traction along with the reformist impetus of urban design, based on the ideal of efficient, functional, sustainable cities that promote a better quality of life for their citizens and on the notion of making urban expansion compatible with natural resources. Using a diachronic analysis (1995–2020) of the orthophoto maps of 38 UGSs included in the last version of the main municipality’s legislation, framed within the Plano Director Municipal (PDM), whose first version was approved in 1994, we assessed the situation and evolution of the UGSs in the centre of Lisbon, Portugal. We conclude that with fewer or more incidences, depending on the periods analysed, the structure of UGSs in the centre of Lisbon has undergone significant changes, whether in terms of the implementation and requalification of existing UGSs or in terms of the increment and quality of UGSs (space created), thus respecting to a lesser or greater degree the strategic lines defined in various plans, as is the case of the PDM itself, but also of other important documents, such as the Carta Estratégica 2010–2024, Estratégia de Reabilitação Urbana 2011–2024 and Estratégia Regional de Lisboa 2030.

Automatic Extraction Method of Landslide Based on Digital Elevation Model and Digital Orthophoto Map Data Combined in Complex Terrain

This study aims to accurately determine the distribution of landslides in the complex terrain of Jiangdingya, Nanyu Township, Zhouqu County, Gansu Province. The digital orthophoto map (DOM) and digital elevation model (DEM) are used to accurately identify landslide areas and analyze associated data. Based on image-based supervised classification, the influence factor constraint analysis is used to further identify and delineate the landslide area. Three mathematical morphology operations—erosion, dilation, and opening—are then applied to automatically identify and extract landslides. Experimental results demonstrate that achieving an accuracy, precision, and recall of 98.02%, 85.24%, and 84.78% shows that it is possible to better avoid interference caused by complex terrain with rich features. High-resolution DEM and DOM data contain rich spectral and texture information. These data can accurately depict geomorphic features of complex terrain and aid in identifying landslide-prone areas when combined with mathematical morphology processing. This contribution is important for identifying landslides in complex terrain and emergency disaster management.