Digital Orthophoto Research Articles

Correction of Terrain Effects on Forest Canopy Height Estimation Using ICESat-2 and High Spatial Resolution Images

The Ice, Cloud, and Land Elevation Satellite–2 (ICESat–2) carries the Advanced Topographic Laser Altimeter System (ATLAS), enabling global canopy height measurements from forest canopy height models (CHMs). Topographic slope is a crucial factor affecting the accuracy of canopy height estimates from ICESat–2 CHMs, but it has not been sufficiently studied. This paper aims to eliminate the influence of slope on canopy height estimates from ICESat–2 data and establishes a method for correcting forest canopy heights based on high spatial resolution digital orthophoto maps (DOM). The cross-track photons are corrected horizontally to eliminate the estimation error. Multi-resolution segmentation is used to segment tree crowns in the DOM, and the distance and relative position between the top of canopy (TOC) photons and the center point of the crown are calculated. TOC photon correction rules are established for different terrains, and the vertical error of the TOC photons is corrected. The results indicate that the vertical error increases exponentially with the slope. The cross-track photon correction and the TOC photon correction method eliminate the effect of slope on canopy height estimates. The cross-track photon correction method reduces the mean absolute error (MAE) and root mean square error (RMSE) of the canopy height estimates by 35.71% and 35.98%, respectively. The TOC photon correction approach further reduces the MAE and RMSE by 23% and 19.23%, respectively. The proposed method has significantly higher accuracy for forest canopy height estimation using ICESat–2 data than the traditional method.

Orthorectification of Fisheye Image under Equidistant Projection Model

The fisheye camera, with its large viewing angle, can acquire more spatial information in one shot and is widely used in many fields. However, a fisheye image contains large distortion, resulting in that many scholars have investigated its accuracy of orthorectification, i.e., generation of digital orthophoto map (DOM). This paper presents an orthorectification method, which first determines the transformation relationship between the fisheye image points and the perspective projection points according to the equidistant projection model, i.e., determines the spherical distortion of the fisheye image; then introduces the transformation relationship and the fisheye camera distortion model into the collinearity equation to derive the fisheye image orthorectification model. To verify the proposed method, high accuracy of the fisheye camera 3D calibration field is established to obtain the interior and exterior orientation parameters (IOPs/EOPs) and distortion parameters of the fisheye lens. Three experiments are used to verify the proposed orthorectification method. The root mean square errors (RMSEs) of the three DOMs are averagely 0.003 m, 0.29 m, and 0.61 m, respectively. The experimental results demonstrate that the proposed method is correct and effective.

Test and Analysis of Vegetation Coverage in Open-Pit Phosphate Mining Area around Dianchi Lake Using UAV-VDVI.

This work aimed to detect the vegetation coverage and evaluate the benefits of afforestation and ecological protection. Unmanned aerial vehicle (UAV) aerial survey was adopted to obtain the images of tailings area at Ma’anshan near the Dianchi Lake estuary, so as to construct a high-resolution Digital Orthophoto Map (DOM) and high-density Dense Image Matching (DIM) point cloud. Firstly, the optimal scale was selected for segmentation by considering the terrain. Secondly, the visible-band difference vegetation index (VDVI) of the classified vegetation information of the tail mining area was determined from the index gray histogram, ground class error analysis, and the qualitative and quantitative analysis of the bimodal index. Then, the vegetation information was extracted by combining the random forest (RF) classification algorithm. Finally, the extracted two-dimensional (2D) vegetation information was mapped to the three-dimensional (3D) point cloud, and the redundant data was eliminated. Fractional vegetation cover (FVC) was counted in the way of surface to point and human–machine combination. The experimental results showed that the vegetation information extracted from the 2D image was mapped to the 3D point cloud in the form of surface to point, and the redundant bare ground information was eliminated. The statistical FVC was 36.06%. The field survey suggested that the vegetation information in the turf dam area adjacent to the open phosphate deposit accumulation area research area was sparse. Relevant measures should be taken in the subsequent mining to avoid ecological damage caused by expanded phosphate mining. In general, applying UAV measurement technology and related 2D and 3D products to detect the vegetation coverage in an open phosphate mine area was of practical significance and unique technical advantages.

Influence of Flight Height and Image Sensor on the Quality of the UAS Orthophotos for Cadastral Survey Purposes

The possibility of using unmanned aircraft systems (UAS) for cadastral survey purposes was investigated in this research. A study site consisting of 26 ground control points (GCP) and checkpoints (CP) was established. The study site was first measured by the classical methods of geodetic surveying, i.e., by the polar method using a total station. After that, all points were additionally measured by the Global Navigation Satellite System (GNSS) Real-Time Kinematic (RTK) method. The GNSS RTK method was used to determine the coordinates of all points in the official map projection of Croatia, HTRS96/TM, while the polar method was used to increase the positional “strength” of points in all directions, i.e., to improve the relative accuracy between them. Using UASs with different image sensor characteristics, the study site was measured by an aerial photogrammetry method at different flight heights with the purpose of obtaining a high-quality digital orthophoto plan (DOF). The absolute orientation of the model was performed using the external orientation data of each digital image based on GNSS and Inertial Measurement Unit (IMU) UAS’s sensors, as well as using GCPs. Achieved precision of obtained DOF, as well as accuracy analysis of aerial photogrammetry was performed by considering the adjusted survey data collected by classical and GNSS RTK methods as true values and comparing them with the coordinates obtained by the aerial photogrammetry method from DOFs. Based on the achieved results and conclusions obtained from the study site, the second field test was performed above a small settlement which served as an area for cadastral survey using the UAS and GNSS RTK method. Again, precision and accuracy were determined, based on which we derived recommendations and conclusions for using UASs for cadastral survey purposes.

Monitoring lava dome extrusion of Merapi Volcano during 2018-2019, using low-cost UAV application

Merapi volcano has a well-known eruption type, namely Merapi type, in which an extruded lava dome collapses and is accompanied by pyroclastic density current (PDC). This type of eruption makes morphological monitoring of the lava dome crucial in the hazard mitigation process. After the VEI 4 eruption in 2010, a new lava dome of Merapi appeared on top of the 2010 lava dome in August 2018 and continuously grew. In November 2019, the lava dome started to collapse outward the crater area. We reported the lava dome morphological monitoring using a UAV (Unmanned Aerial Vehicle) photogrammetry conducted from August 2018 to February 2019. This UAV monitoring provides processed aerial photo data in Digital Terrain Model (DTM) and orthophoto with low operating costs and short data acquisition time. The lava domes erupted from the same eruptive canter within this period and grew evenly in all directions. The 2018-2019 Merapi lava dome has basal ratio of 0.183 to 0.290 with height of 11 to 41 m, respectively. Volume changed from 33,623 m3 in August 2018 to 658,075 m3 in February 2019, suggesting growth rate at ~3,500 m3/day. The lava base filled the crater base area (0.21 km2) and started to collapse outward in November 2019.

Processing CORONA image for generation of Digital Elevation Model (DEM) and orthophoto of Bilaspur district, Himachal Pradesh

Processing CORONA image for generation of Digital Elevation Model (DEM) and orthophoto of Bilaspur district, Himachal Pradesh

Gully Morphological Characteristics and Topographic Threshold Determined by UAV in a Small Watershed on the Loess Plateau

Gully erosion is an important sediment source in small watershed, and causes severe land degradation, particularly in semi-arid regions. Accurately measuring gully morphological characteristics, and determining its topographic threshold, are vital for gully erosion simulation and control. In this study, 910 gullies were visually interpreted by unmanned aerial vehicle (UAV) technology combined with field measurement. Ten gully morphological characteristics were extracted from the digital orthophoto map (DOM) and digital elevation model (DEM) generated by UAV images, including gully length (L), circumference (C), plane area (PA), surface area (SA), volume (V), depth (D), top width (TW), mean width (MW), cross-sectional area (CSA), and ratio of top width to depth (TW/D). The morphological characteristics of 30 reachable gullies were measured by a real time kinematic (RTK) to validate the parameters extracted from the UAV images. The topographic thresholds were determined based on the local slope gradient (S) and upland drainage area (A), using a dataset of 365 gully heads and their corresponding land-use types. The results show that the mean absolute percentage errors (MAPE) of the 2D and 3D gully characteristics are less than 10% and 20%, respectively, demonstrating a high accuracy of gully characteristic extraction from UAV images. Gully V is significantly related to the other nine parameters. Significant power functions were fitted between V, and L, C, PA, and SA. The gully volume could be well-estimated by SA (V = 0.212 SA0.982), with a R2 of 0.99. For all land-use types, the topographic threshold could be described as S = 0.61 A0.48, implying that water erosion is the dominant process controlling gully erosion in this region. The topographic threshold is land-use-dependent, and shrubland is hardest for gully incision, followed by grassland and cropland. The results are helpful to rapidly estimate gully erosion, and identify the areas for gully erosion mitigation in small watershed.

Spatial Modelling of Vineyard Erosion in the Neszmély Wine Region, Hungary Using Proximal Sensing

Intense soil erosion in the northern part of the Gerecse Hills, Hungary, is causing significant damage to vineyards in the area. Three vineyards in the Neszmély Wine Region were investigated to quantify the amount of eroded soils. The method was based on monitoring vineyards for one-year between July 2019 and June 2020. Every season, a set of photographs of the vineyards were taken from an unmanned aerial vehicle. The images were processed in a photogrammetric workflow to produce high-resolution digital terrain models (DTMs) and orthophotos, which were used to estimate the soil loss using the Universal Soil Loss Equation (USLE) model. Particular attention was paid to the effect of seasonal variation in vegetation cover and rainfall, and the erosion control effect of the inter-row grassing already applied in the vineyards was also modelled. The results confirm and quantify the extent to which intense summer rainfall has a more significant effect on erosion compared to autumn or winter rainfall.

Low-Altitude Photogrammetry and Remote Sensing in UAV for Improving Mapping Accuracy

The low-altitude photogrammetry technology of unmanned aerial vehicles (UAVs) is widely used in many fields, but the absence of analysis and research affects the accuracy of its data products. At the same time, low-altitude photogrammetry faces the problem of low elevation positioning accuracy. The network space triangulation adjustment in the beam technique region is considered to eliminate perspective distortion in non-overlapping areas. This paper explains the key technologies of low-altitude photography and remote sensing mapping of UAVs, rectifies the distortion difference of remote sensing images, and then carries out grid division on the image according to the improved APAP (as-projective-as-possible warp) matching method. Next, it solves each grid homography matrix, linearizes the homography matrix, and carries out image matching according to the linearized homography matrix, which can effectively weaken the ghosting phenomenon during image matching. The network space triangulation adjustment in the beam technique region is considered to eliminate perspective distortion in non-overlapping areas. The two measurement areas’ accuracy level is analyzed using digital line drawing and digital orthophoto images (DOIs). Finally, the experimental results indicate that the image matching algorithm proposed in this paper has strong reliability and can substantially increase photogrammetric elevation positioning.

Dynamic Monitoring of Laohugou Glacier No. 12 with a Drone, West Qilian Mountains, West China

Laohugou glacier No. 12 (LHG12), located in the northeast of the Qinghai–Tibet Plateau, is the largest valley glacier in the Qilian mountains. Since 1957, LHG12 has shrunk significantly. Due to the limitations of in situ observations, simulations and investigations of LHG12 have higher levels of uncertainty. In this study, consumer-level, low-altitude microdrones were used to conduct repeated photogrammetry at the lower part of LHG12, and a digital orthophoto map (DOM) and a digital surface model (DSM) with a resolution at the centimeter scale were generated, from 2017 to 2021. The dynamic parameters of the glacier were detected by artificial and automatic extraction methods. Using a combination of GNSS and drone-based data, the dynamic process of LHG12 was analyzed. The results show that the terminus of LHG12 has retreated by 194.35 m in total and by 19.44 m a−1 on average during 2008–2021. The differential ablation leading to terminus retreat distance markedly increased during the study period. In 2019–2021, the maximum annual surface velocity was 6.50 cm day−1, and during ablation season, the maximum surface velocity was 13.59 cm day−1, 52.17% higher than it is annually. The surface parameters, motion, and mass balance characteristics of the glacier had significant differences between the west and east branches. The movement in the west branch is faster than it is in the east branch. Because of the extrusion of the two ice flows, there is a region with a faster surface velocity at the ablation area. The ice thickness of LHG12 is decreasing due to intensified ablation, leading to a deceleration in the surface velocity. In large glaciers, this phenomenon is more obvious than it is in small glaciers in the Qilian mountains.

Optimizing Local Alignment along the Seamline for Parallax-Tolerant Orthoimage Mosaicking

Orthoimage mosaicking with obvious parallax caused by geometric misalignment is a challenging problem in the field of remote sensing. Because the obvious objects are not included in the digital terrain model (DTM), large parallax exists in these objects. A common strategy is to search an optimal seamline between orthoimages, avoiding the majority of obvious objects. However, stitching artifacts may remain because (1) the seamline may still cross several obvious objects and (2) the orthoimages may not be precisely aligned in geometry when the accuracy of the DTM is low. While applying general image warping methods to orthoimages can improve the local geometric consistency of adjacent images, these methods usually significantly modify the geometric properties of orthophoto maps. To the best of our knowledge, no approach has been proposed in the field of remote sensing to solve the problem of local geometric misalignments after orthoimage mosaicking with obvious parallax. In this paper, we creatively propose a method to optimize local alignment along the seamline after seamline detection. It consists of the following main processes. First, we locate regions with geometric misalignments along the seamline based on the similarity measure. Second, for any one region, we find one-dimensional (1D) feature matches along the seamline using a semi-global matching approach. The deformation vectors are calculated for these matches. Third, these deformation vectors are robustly and smoothly propagated into the buffer region centered on the seamline by minimizing the associated energy function. Finally, we directly warp the orthoimages to eliminate the local parallax under the guidance of dense deformation vectors. The experimental results on several groups of orthoimages show that our proposed approach is capable of eliminating the local parallax existing in the seamline while preserving most geometric properties of digital orthophoto maps, and that it outperforms state-of-the-art approaches in terms of both visual quality and quantitative metrics.

Characteristics, recent evolution, and ongoing retreat of Hunt Fjord Ice Shelf, northern Greenland

AbstractArctic ice shelves have declined over the past several decades, one of many indications of a rapidly changing cryosphere. Here we use a collection of off-nadir Landsat 8 images, a 1978 digital orthophotograph and photogrammetrically derived DEM, satellite altimetry and other data to examine the causes of an Arctic ice-shelf retreat in northernmost Greenland, the Hunt Fjord Ice Shelf (HFIS). HFIS has several distinct provenance regions comprised of glacier-derived ice and corrugated multi-decadal fast ice, with varying ice thicknesses (5–64 m). Available imagery shows little change in HFIS between 1978 and 2012, after which several midsummer calving events occurred (2012, 2016 and 2019) that reduced the HFIS by 42.5 km2 (~56%). Shelf area losses began as the number of surface melt days on the adjacent ice sheet more than doubled relative to the 1980s. Recent calving events also occurred during open-water periods at the ice-shelf front. Prior to mid-2012, there were no calving events during similar open-water periods. HFIS tributary glaciers have thinned by 3–20 m near their grounding zones, and may have accelerated since the 1980s, likely due to increased basal melting from contact with warm Atlantic Water.

CONSISTENCY CHECKING METHOD OF SURFACE COVERAGE DATA RESULTS

Abstract. Combined with the acceptance of the surface coverage data results of the national geographic situation monitoring project, as well as the consistency requirements between the surface coverage data results and the background data, the "three tone" data results, the digital orthophoto data and the field investigation and verification data, this paper puts forward the quality control method of the surface coverage classification data results by analyzing the quality problems found in the inspection. This method provides a technical reference for the quality control of the national geographic situation monitoring project and a method basis for quality inspection.

RESEARCH ON THE KEY TECHNOLOGY OF UAV IN THE ACCURACY ASSESSMENT OF LAND COVER CLASSIFICATION

Abstract. This paper uses UAV low-altitude aerial photography technology, equipped with orthophoto camera, tilt camera and LiDAR camera, to obtain and process high-precision evaluation data, which is used to evaluate the correctness of land cover classification. Through the design of aerial photography scheme and data collection and processing, the rapidly acquired digital orthophoto, oblique 3D model data and laser LiDAR form a complete set of technical processes, which can relatively accurately and objectively monitor the quality of surface data for the existing geographical conditions. situation is assessed. The main purpose of this study is to further enrich the technical means of quality inspection of surveying and mapping products, to improve the technical level of quality inspection and acceptance of geographic condition monitoring data, and to improve the accuracy and reliability of data quality evaluation.

Combining Canopy Coverage and Plant Height from UAV-Based RGB Images to Estimate Spraying Volume on Potato

Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume.

High-precision monitoring of urban structures to understand changes in multiple ecosystem services

To safeguard the well-being of urban dwellers, it is vital to restore, protect and enhance urban green infrastructures (uGI), their related ecosystem services (ES) and the associated benefits for a large number of inhabitants. This study maps and monitors land cover between 2012 and 2018 in the fast-growing German city of Leipzig to produce precise information using OBIA and very high-resolution digital orthophotos. Based on this, this research pinpoints spatially differentiated multiple ES. Research has revealed that essential ES, which comprise regulating, socio-cultural and cultural-aesthetic services, have a multifunctional impact on the human urban habitat. The study provides insight into each ES type by evaluating specific classes of objects within the urban environment in a spatially explicit way and at a very high scale of resolution. In doing so, it illustrates variations in the provision of ES and renders visible disparities in the accessibility to uGI in Leipzig. By analysing the number and stands of trees and their respective height development, the study confirms that intensive management is successfully rejuvenating the urban forest, but also that foliage in this forest is suffering from drought. The mapping procedure reveals a high spatial and temporal variation in the rates of carbon storage. This is also the case for the provision of recreation areas which has an impact on the equitable distribution of ES to Leipzig’s inhabitants. Residential areas with a relatively high uGI on the outskirts of the city actually register lower market rents and rent growth rates than in those districts which lie closer to the city centre and have a comparably lower uGI. Thus, market rents and uGI have become decoupled in the fast growing city. In order to ensure and maintain the well-being of all residents in a fair way, fast growing cities like Leipzig must make even greater efforts in urban planning.

Deformation Monitoring and Spatiotemporal Evolution of Mining Area with Unmanned Aerial Vehicle and D-InSAR Technology

The extraction of underground mineral sources has a significant negative impact on the local environment, results in land surface subsidence. As far as subsidence monitoring technology is concerned, leveling is the most accurate. However, leveling can only obtain discrete point data but not the whole area information of the subsidence basin. In this study, Differential Interferometric Synthetic Aperture Radar (D-InSAR) combined with Unmanned Aerial Vehicle (UAV) technology is used to study the subsidence characteristics of the whole working panel. In this analysis, the Huainan mining area is tested as a research area; measured data are compared to the elevation accuracy of the Digital Surface Model (DSM) data, which can be considered for the subsequent works of the mining area. Based on the subsidence affected area, the ground object-type information is recorded to provide basic information for the ecological restoration work after mining so that the data before and after mining can be obtained synchronously. Finally, the differential interference results and Digital Orthophoto Map (DOM) data are combined to assess the spatiotemporal evolution of working panel subsidence and its influence on surface features. The main novelty of the proposed work is combining UAV and D-InSAR to get more accurate analysis of mining subsidence. It can be done using the proposed method.

Individual Maize Location and Height Estimation in Field from UAV-Borne LiDAR and RGB Images

Crop height is an essential parameter used to monitor overall crop growth, forecast crop yield, and estimate crop biomass in precision agriculture. However, individual maize segmentation is the prerequisite for precision field monitoring, which is a challenging task because the maize stalks are usually occluded by leaves between adjacent plants, especially when they grow up. In this study, we proposed a novel method that combined seedling detection and clustering algorithms to segment individual maize plants from UAV-borne LiDAR and RGB images. As seedlings emerged, the images collected by an RGB camera mounted on a UAV platform were processed and used to generate a digital orthophoto map. Based on this orthophoto, the location of each maize seedling was identified by extra-green detection and morphological filtering. A seed point set was then generated and used as input for the clustering algorithm. The fuzzy C-means clustering algorithm was used to segment individual maize plants. We computed the difference between the maximum elevation value of the LiDAR point cloud and the average elevation value of the bare digital terrain model (DTM) at each corresponding area for individual plant height estimation. The results revealed that our height estimation approach test on two cultivars produced the accuracy with R2 greater than 0.95, with the mean square error (RMSE) of 4.55 cm, 3.04 cm, and 3.29 cm, as well as the mean absolute percentage error (MAPE) of 3.75%, 0.91%, and 0.98% at three different growth stages, respectively. Our approach, utilizing UAV-borne LiDAR and RGB cameras, demonstrated promising performance for estimating maize height and its field position.

Research on Damage Localization of Steel Truss–Concrete Composite Beam Based on Digital Orthoimage

Most structural health monitoring is carried out for a limited number of key measurement points of a bridge, and incomplete measurement data lead to incomplete mechanical equation inversion results, which is a key problem faced in bridge damage identification. The ability of digital images to holographically describe structural morphology can effectively alleviate the problem of damage identification due to incomplete test data. Based on digital image processing technology, a matrix similarity damage identification method based on a structural digital orthoimage was proposed. Firstly, a steel truss–concrete composite beam specimen with a complex support bar system was designed and fabricated in the laboratory, and the digital orthoimage of the test beam was obtained by the perspective transformation of the original image of the test beam. The body contour of the structure was extracted from the digital orthoimage of the test beam, and wavelet threshold denoising was performed on the lower edge profile to obtain the deflection curves of the structure under different working conditions. The verification results show that the maximum error of the deflection curve is 3.42%, which proves that the digital orthoimage can accurately and completely reflect the deformation of the structure. Finally, based on the digital orthophoto of the test beam, a matrix similarity test before and after the damage was carried out, and the results show that the singularities of the similarity distribution are consistent with the location of the damage; furthermore, the accurate positioning of the damage in different working conditions is achieved.

Urban Change Detection from Aerial Images Using Convolutional Neural Networks and Transfer Learning

Urban change detection is an important part of sustainable urban planning, regional development, and socio-economic analysis, especially in regions with limited access to economic and demographic statistical data. The goal of this research is to create a strategy that enables the extraction of indicators from large-scale orthoimages of different resolution with practically acceptable accuracy after a short training process. Remote sensing data can be used to detect changes in number of buildings, forest areas, and other landscape objects. In this paper, aerial images of a digital raster orthophoto map at scale 1:10,000 of the Republic of Lithuania (ORT10LT) of three periods (2009–2010, 2012–2013, 2015–2017) were analyzed. Because of the developing technologies, the quality of the images differs significantly and should be taken into account while preparing the dataset for training the semantic segmentation model DeepLabv3 with a ResNet50 backbone. In the data preparation step, normalization techniques were used to ensure stability of image quality and contrast. Focal loss for the training metric was selected to deal with the misbalanced dataset. The suggested model training process is based on the transfer learning technique and combines using a model with weights pretrained in ImageNet with learning on coarse and fine-tuning datasets. The coarse dataset consists of images with classes generated automatically from Open Street Map (OSM) data and the fine-tuning dataset was created by manually reviewing the images to ensure that the objects in images match the labels. To highlight the benefits of transfer learning, six different models were trained by combining different steps of the suggested model training process. It is demonstrated that using pretrained weights results in improved performance of the model and the best performance was demonstrated by the model which includes all three steps of the training process (pretrained weights, training on coarse and fine-tuning datasets). Finally, the results obtained with the created machine learning model enable the implementation of different approaches to detect, analyze, and interpret urban changes for policymakers and investors on different levels on a local map, grid, or municipality level.