Manual Digitization Research Articles

Extent, accuracy and repeatability of bare sand and vegetation cover in dunes mapped from aerial imagery is highly variable

Vegetation cover on coastal sand dunes has been increasing worldwide since at least the 1940s. Analysis of aerial and satellite imagery has been the principal source used to measure this change, however no studies have systematically evaluated the accuracy of remotely sensed estimates. Using established land cover classification methods and in-situ field measurements, we show that both the extent and accuracy of remotely sensed areas of bare sand and vegetation in dunes varies with image resolution and classification method. We found that supervised methods of classification (semi-automatic), whilst mapping a greater extent of bare sand and being more accurate than manual digitisation, had poor repeatability, exhibiting a relatively large range of bare sand and vegetation extent between classifications replicated under the same conditions. In contrast, areas of bare sand and vegetation classified by manual digitisation had high repeatability but a relatively low percentage of observed agreement with data collected in the field. For all classification methods, observed agreement with field data generally increased with image resolution. Our results demonstrate that users of land classification data in dunes should be cautious when interpreting trends of bare sand and vegetation cover due to substantial repeatability error in supervised classification methods, and relatively poor observed agreement with field data of manual classification. We recommend that analysis of bare sand and vegetation cover in dunes should be based on multiple replicates using supervised classification, employing the highest resolution imagery available and that all results presented should also include the range measured by multiple replicates.

A lightweight keypoint matching framework for insect wing morphometric landmark detection

Geometric morphometrics has become an important approach in insect morphology studies because it capitalizes on advanced quantitative methods to analyze shape. Shape could be digitized as a set of landmarks from specimen images. However, the existing tools mostly require manual landmark digitization, and previous works on automatic landmark detection methods do not focus on implementation for end-users. Motivated by that, we propose a novel approach for automatic landmark detection, based on visual features of landmarks and keypoint matching techniques. While still archiving comparable accuracy to that of the state-of-the-art method, our framework requires less initial annotated data to build prediction model and runs faster. It is lightweight also in terms of implementation, in which a four-step workflow is provided with user-friendly graphical interfaces to produce correct landmark coordinates both by model prediction and manual correction. The utility iMorph is freely available at https://github.com/ha-usth/InsectWingLandmark, currently supporting Windows, MacOS, and Linux.

Monitoring 23-year of shoreline changes of the Zengwun Estuary in Southern Taiwan using time-series Landsat data and edge detection techniques

More than 80% of sandy beaches in Taiwan have been experiencing a severe recession, although the sediment discharge of rivers in Taiwan is significantly higher than the world average producing almost 2% of global fluvial sediment discharge. This contradiction is primarily due to the widespread constructions of reservoirs and intensive anthropogenic activities in coastal regions. In addition, coasts are particularly vulnerable to hazards due to climate change, such as sea-level rise, as they are located at the transition zone of terrestrial and marine environments. Along with the fact that Taiwan is an island and is one of the most climate-vulnerable regions globally, coastal management and sustainability are nationally critical topics, especially considering the ongoing reformation and legislation of Taiwan's coastal conservation laws.As stated in the Sustainable Development Goals (SDGs) goal 14, accurate and continuous shoreline positions information is essential for coastal conservation. However, by reviewing previous global studies and projects commissioned by the Taiwanese government aiming at monitoring shoreline changes, they usually exhibit several limitations, such as limited band selections or conservative band ratio-derived water indices, relying on either manual digitization or simple thresholding methods, focusing on either artificial or smoothly shaped coasts, and using images acquired at considerably different tidal height levels.Therefore, in the present study, a subpixel shoreline extraction approach based on a sustainable cross-generation dataset and a robust edge detection algorithm is proposed. This approach is exemplified by the Zengwun River Estuary located in southwestern Taiwan—Taiwan's most critical coastal preservation region. By quantitatively analyzing the resultant time-series shoreline positions from 1999 to 2021, several hotspots of shoreline recession have been identified: an extreme erosional rate up to −69.4 m year−1 is revealed in the northern sand bank; while the offshore sand bar demonstrates an overall landward retreat rate of −35.4 ± 1.24 m year−1.

The Association Between Hip-Shoulder Separation Angles and Technique Characteristics in World-Class High Jumpers.

Hip-shoulder separation (H-Ssep) has been widely researched in many sporting activities (e.g., golf) to provide information on the contribution of torso rotation to performance and injury. Although it is necessary for high jumpers to generate significant long-axis rotation to successfully clear the bar, limited information exists on H-Ssep for high jump athletes. As such, this study aimed to (a) characterize the H-Ssep of world-class high jump athletes during competition, (b) determine if differences exist between male and female athletes and (c) to examine the relationship between H-Ssep and the biomechanical parameters used to describe high jump technique. Twenty-nine world-class high jumpers (17 males, 12 females) were video recorded (frame rate: 120–200 Hz) during the 2017 and 2018 World Athletics Championship finals. H-Ssep was quantified at touchdown (TD) and take-off (TO) following manual digitizing (SIMI motion) and a number of other common biomechanical parameters were computed. The observed levels of H-Ssep at TD (−46±12°) and TO (16 ±11°) were in line with those reported previously for other sports. The magnitude of H-Ssep varied between individuals and showed significant associations with other approach and take-off characteristics. Significant differences in H-Ssep were not evident between male and female athletes despite significant differences in other performance- and technique-related parameters. These findings highlight the divergent take-off characteristics of world-class performers and their reliance on hip-shoulder interactions when generating long axis rotation. Coaches should be mindful of the mechanical and physical consequences of H-Ssep when developing technical models, conditioning interventions and coaching strategies.

Approaching automated applicator digitization from a new angle: Using sagittal images to improve deep learning accuracy and robustness in high-dose-rate prostate brachytherapy

PURPOSETo automate the segmentation of treatment applicators on computed tomography (CT) images for high-dose-rate (HDR) brachytherapy prostate patients implanted with titanium needles with the goals of improving plan quality and reducing the patient's time under anesthesia. METHODSThe investigation was performed using 57 retrospective, interstitial prostate treatments randomly assigned to training (n = 27), validation (n = 10), and testing (n = 20). Unique to this work, the CT image set was reformatted into 2D sagittal slices instead of the default axial orientation. A deep learning-based segmentation was performed using a 2D U-Net architecture followed by a density-based linkage clustering algorithm to classify individual catheters in 3D. Potential confounders, such as gold seeds and conjoined applicators with intersecting needle geometries, were corrected using a customized polynomial fitting algorithm. The geometric agreement of the automated digitization was evaluated against the clinically treated manual digitization to measure tip and shaft errors in the reconstruction. RESULTSThe proposed algorithm achieved tip and shaft agreements of -0.1 ± 0.6 mm (range -1.8 mm to 1.4 mm) and 0.13 ± 0.09 mm (maximum 0.96 mm), respectively on a data set with 20 patients and 353 total needles. Our method was able to separate all intersecting applicators reliably. The time to generate the automated applicator digitization averaged approximately 1 min. CONCLUSIONSUsing sagittal instead of axial images for 2D segmentation of interstitial brachytherapy applicators produced submillimeter agreement with manual segmentation. The automated digitization of interstitial applicators in prostate brachytherapy has the potential to improve quality and consistency while reducing the patient's time under anesthesia.

Agreement Between Sagittal Foot and Tibia Angles During Running Derived From an Open-Source Markerless Motion Capture Platform and Manual Digitization.

Several open-source platforms for markerless motion capture offer the ability to track 2-dimensional (2D) kinematics using simple digital video cameras. We sought to establish the performance of one of these platforms, DeepLabCut. Eighty-four runners who had sagittal plane videos recorded of their left lower leg were included in the study. Data from 50 participants were used to train a deep neural network for 2D pose estimation of the foot and tibia segments. The trained model was used to process novel videos from 34 participants for continuous 2D coordinate data. Overall network accuracy was assessed using the train/test errors. Foot and tibia angles were calculated for 7 strides using manual digitization and markerless methods. Agreement was assessed with mean absolute differences and intraclass correlation coefficients. Bland-Altman plots and paired t tests were used to assess systematic bias. The train/test errors for the trained network were 2.87/7.79 pixels, respectively (0.5/1.2cm). Compared to manual digitization, the markerless method was found to systematically overestimate foot angles and underestimate tibial angles (P < .01, d = 0.06-0.26). However, excellent agreement was found between the segment calculation methods, with mean differences ≤1° and intraclass correlation coefficients ≥.90. Overall, these results demonstrate that open-source, markerless methods are a promising new tool for analyzing human motion.

An inventory of Norway's glaciers and ice-marginal lakes from 2018–19 Sentinel-2 data

AbstractWe used Sentinel-2 satellite imagery at 10 m resolution to map the extent of Norway's glaciers and ice-marginal lakes over 2018–19. We applied a standardized semi-automated band ratio method to derive glacier outlines and ice-marginal lakes. To optimise the results, we manually edited the ice-lake interfaces, debris, snow and parts of the glaciers situated under shadow. We compared our Sentinel-2 derived outlines with very high-resolution aerial orthophotos and Pléiades satellite orthoimages. Glaciers larger than 0.3 km2have area differences within 7%, whereas values are larger for smaller glaciers. The orthophotos and orthoimages provide more details and a higher mapping accuracy for individual glaciers, but require manual digitisation, have smaller spatial and temporal coverage and can have adverse snow conditions. We found a total glacier area of 2328 ± 70 km2of which the ten largest glaciers accounted for 52%. The glacier area decreased 15% since the previous inventory (Landsat data from 1999 to 2006), the reduction being largest in northern Norway (22%) compared to southern Norway (10%). We detected more than 2000 previously undetected smaller glaciers and ice patches (covering 37 km2) and 360 new ice-marginal lakes.

CHANGE DETECTION OF THE LAND USE LAND COVER CATEGORIES AT LEVEL-II CLASSIFICATION USING MULTI TEMPORAL SATELLITE DATA: A CASE STUDY OF JODHPUR DISTRICT, RAJASTHAN.

Transformation of Land use Land cover is a dynamic process that takes place on the Earth’s surface and becomes a basic component in current strategies to manage natural resources and to monitor environmental changes. Remote sensing and GIS are important tools and techniques for monitoring and assessment of land use land cover change status, which can be helpful to decide the strategies for productive use of land for sustainable development. Therefore, it is important to detect the land use or land cover changes for sustaining growth and development of any region. The objective of this paper is to analyse the land use land cover changes over the last 24 years in Jodhpur district, Rajasthan, using multi season satellite data of 30 meter spatial resolution namely, Landsat-5 TM for 1993 and Landsat-8 OLI/TIRS for 2017. This paper has prepared the land use land cover status map on 1:50000 scale with the help of manual digitization technique for the years 1993 and 2017. Later, the change detection in land use land cover changes from 1993 to 2017 has been calculated using ArcGIS software. Land use land cover classes are classified into various categories such as built-up land, kharif crop, Rabi crop, double crop, fallow land, forest, scrub land, dunes, Barren/Rocky area, Quarrying, Lake/Reservoir/Tank, and others for the years of 1993 and 2017. The result of the study shows the expansion of agricultural land, built-up land, Quarrying and other manmade area during this time period and at the same time scrub land, dunes and barren rocky area are decreased. This has significant impact on the livelihood of the local community.

Automatic Extraction of Unmanned Aerial Vehicles (UAV)-based Cadastral Map: Case Study in AL-Shatrah District- Iraq

Cadastral map environment is directed, more than ever before, towards Artificial Intelligence use to produce fast and accurate maps and keep up with the huge population growth. The traditional approach currently in production of maps is expensive and effort-intensive in addition to be considered as highly time-consuming process. UAV-based cadastral mapping imagery that use automatic techniques are newly being exploited to accelerate the process of production or updating. The state-of-the-art intelligent algorithms are capable to extract land boundaries from images better than conventional techniques. This paper presents an automatic workflow of cadastral map production based on land boundary and automatic feature extraction from UAV-based imageries. The developed workflow involved four steps: (i) Flight planning and Ground Control Points (GCPs) observation, (ii) image pre-processing, (iii) image segmentation (iv) boundary detection and extraction (classification), (v) postprocessing, and (vi) accuracy assessment. The study area includes geometrical and affect spectral reflectivity (slums) covered by 206 images those captured using a quad-rotary UAV (DJI Phantom 4 pro). The mosaic image of the Area of Interest (AOI) was produced by following Structure from Motion /Multi-view Stereo (SfM-MVS) photogrammetry which covers 0.592 km2 ground area using pix4DMapper software. The Multi-Resolution Segmentation (MRS) algorithm was applied for object generation and later both spectral and geometrical information (area, brightness, border and the normalized digital surface model NDSM)) utilised to extract the boundaries by Object Based Image Analysis (OBIA) rule-based expert systems in eCognition software. The most exciting challenge in this AOI was buildings separation depending on number of buildings using the QGIS software. The accuracy assessment of the obtained results showed that 88% of the extracted boundaries were correct following automatic extraction routine when compared to manual digitizing. This approach can highly compensate time, efforts, and low accuracy outcomes from traditional surveying and manual registration approaches applied in Iraqi institutes nowadays. The presented approach can definitely help to speed up map production phase and keep pace up with population expansion and modern technologies.

Analisis Spasial Terhadap Tingkat Kerawanan Banjir DAS Kenali Kecil Menggunakan Metode SNI 8197:2015

Floods are the most common disasters in almost all places, including Indonesia and Jambi City in particular. Areas that are directly exposed to the threat of flooding are in the Watershed. Land conversion is one of the causes of flooding in the Kenali Kecil watershed. This study uses the standard SNI 8197:2015 regarding the Flood Hazard Mapping Method 1:50,000 and 1:25,000 scale. The approach used is landscape analysis, which emphasizes the process of forming natural systems on the earth's surface. This study aims to analyze and produce a map of the level of flood vulnerability in the Kenali Kecil watershed area. The data needed in this study are ten days of rainfall data, slope data and land cover data. Slope data was obtained from DEMNas data processing and land cover data was obtained from manual digitizing of Google Earth's High-Resolution Satellite Imagery data. Data processing uses ArcMap software with scoring and overlay techniques. The results showed that the Kenali Kecil watershed had a dendritic pattern with an area of 15.68 km2 and located in 5 villages, namely: Bagan Pete, Kenali Besar, Penyengat Rendah, Mendalo Darat and Pematang Gajah. The basic rainfall of the Kenali Kecil watershed is 204.21 mm, the slope is mostly &gt;4% and the land cover is mostly residential. Based on the SNI 8197:2015 procedure and the results of the scoring and overlay process with ArcMap, the flood susceptibility level of the Kenali Kecil watershed is at a medium hazard level of 8.25 km2 (52.6%) and a height of 7.43 km2 (47.4%).

Alpine glacier surface velocity measurement from UAV imagery – examining the effect of image resolution on the accuracy of results

The reliability and validity of the Glacier Surface Velocity (GSV) measurement results based on remote sensing datasets depends on the quality and spatial resolution of the image used. The typical pixel size of space-borne satellite imagery is often larger than the annual and inter-annual displacement of small alpine debris-covered glaciers. In addition, the pixel size of medium resolution satellite images (10–30 m), limits the size of a feature that can be matched. This is even more of an issue for glaciers located in arid and semi-arid environments (e.g. glaciers in Iran and high mountains of Asia) where flow velocities are not exceeding a few metres per year. Consequently, high-resolution data such as Unmanned Aerial Vehicle (UAV) images are required to calculate the surface velocity of such glaciers. However, the optimal resolution of UAV images is one of the most important challenges in GSV measurement. This paper explores the influence of UAV image resolution on the quality of GSV results. Analysis was carried out at 19 different resolutions of UAV images from 10 to 100 cm with a 5 cm bin over a debris-covered glacier, in Iran. COSI-Corr algorithm was used to perform the image correlation. To evaluate the accuracy of obtained results, manual digitisation was performed and the differences between manual GSV and frequency cross-correlator were assessed for all data sets. Moreover, displacement calculated for a stagnant off-glacier area was evaluated. While the quality of the results of the images between 10 and 30 cm is substantially the same, the obtained results indicate that the best result of GSV was not obtained using the finest image resolution. Results revealed that the highest correspondence between the measured GSV and manual digitisation was obtained in a 30 cm spatial resolution image. In addition, the 30 cm image resolution shows the minimum uncertainty over the off-glacier static area. Obtained results revealed that using too fine resolution images will lead to computational redundancy, while no improvement is observed in the accuracy of GSV results.

Automatic Markerless Motion Detector Method against Traditional Digitisation for 3-Dimensional Movement Kinematic Analysis of Ball Kicking in Soccer Field Context.

Kicking is a fundamental skill in soccer that often contributes to match outcomes. Lower limb movement features (e.g., joint position and velocity) are determinants of kick performance. However, obtaining kicking kinematics under field conditions generally requires time-consuming manual tracking. The current study aimed to compare a contemporary markerless automatic motion estimation algorithm (OpenPose) with manual digitisation (DVIDEOW software) in obtaining on-field kicking kinematic parameters. An experimental dataset of under-17 players from all outfield positions was used. Kick attempts were performed in an official pitch against a goalkeeper. Four digital video cameras were used to record full-body motion during support and ball contact phases of each kick. Three-dimensional positions of hip, knee, ankle, toe and foot centre-of-mass (CMfoot) generally showed no significant differences when computed by automatic as compared to manual tracking (whole kicking movement cycle), while only z-coordinates of knee and calcaneus markers at specific points differed between methods. The resulting time-series matrices of positions (r2 = 0.94) and velocity signals (r2 = 0.68) were largely associated (all p < 0.01). The mean absolute error of OpenPose motion tracking was 3.49 cm for determining positions (ranging from 2.78 cm (CMfoot) to 4.13 cm (dominant hip)) and 1.29 m/s for calculating joint velocity (0.95 m/s (knee) to 1.50 m/s (non-dominant hip)) as compared to reference measures by manual digitisation. Angular range-of-motion showed significant correlations between methods for the ankle (r = 0.59, p < 0.01, large) and knee joint displacements (r = 0.84, p < 0.001, very large) but not in the hip (r = 0.04, p = 0.85, unclear). Markerless motion tracking (OpenPose) can help to successfully obtain some lower limb position, velocity, and joint angular outputs during kicks performed in a naturally occurring environment.

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow &amp; Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PDF

Site Selection of Isolation Hospital for Coronavirus Patients in Nile Delta, Egypt, Using GIS Technology

Coronavirus disease (COVID‐19) is a viral infection caused by the SARS‐CoV‐2 virus that first surfaced in December 2019. According to the World Health Organization, most persons infected with this virus suffer from mild to severe respiratory infections and recover without specific treatment or hospitalization. Some people, however, may acquire serious illnesses that need medical attention and isolation facilities. This paper investigates the use of multi‐criteria decision analysis (MCDA) based on GIS technology to determine the optimal site selection for isolation hospitals for coronavirus patients in Nile Delta region in Egypt using the fuzzy analytical hierarchy process (F‐AHP) and the weighted overlay tool analysis method (WOA). The research of isolation hospital site selection in Nile Delta governorates in Egypt is one of the areas that have received insufficient attention due to the current global coronavirus epidemic. Several criteria are applied to identify and select the isolation hospital location, including World Health Organization regulations, Egyptian Ministry of Health conditions, previous research studies, and field visits. Geodatabase is created using ArcGIS Pro software, and manual digitization is done. As a conclusion of the study, numerous additional optimal sites for isolated hospitals have been found and chosen. There are around 29 proposed ideal sites for isolated hospitals utilizing F‐AHP and approximately 24 sites using WOA approach in Nile Delta region. These planned hospital locations might be permanent as a central hospital or temporary, to be relocated after the epidemic is over. The paper emphasizes the need to use the study criteria while selecting and defining the location of coronavirus isolation hospitals.

Geological map feature extraction using object detection techniques - a comparative analysis

Conducting a geological field survey at the initial stage is an important step in geo-oriented projects and construction. Therefore, better and more accurate solutions are only possible with field analysis and proper modeling. The geological modeling process takes a long time, especially depending on the area of interest. It is inefficient to digitize 2D geological maps with traditional software that uses manual user interaction. This paper proposes a state-of-the-art feature detection methodology for detecting geological features on high-resolution maps. With the development of efficient deep learning algorithms and the improvement of hardware systems, the accuracy of detecting specific objects in digital images, such as human facial features, has reached more than 90%. Current object detection models based on convolutional neural networks cannot be directly applied to high-resolution geological maps due to the input image size limitations of conventional object detection solutions, mostly limited by hardware resources. This paper proposes a sliding window method for character detection of geological features. Detection models are trained using transfer learning with You Look Only Once-v3 (YOLO-v3), Single Shot Multi-Box Detector (SSD), Faster-Region-based Convolutional Neural Network (Faster-RCNN), and Single Shot Multi-Box Detector_RetinaNet (SSD_RetinaNet). All models provide competitive success rates with an average precision (AP) of 0.96 on YOLOv3, 0.88 AP on EfficientNet, 0.92 AP on Faster- RCNN, and 0.97 AP on SSD_RetinaNet. YOLOv3 outperformed the best detection over SSD according to F1 recall and score. Since the input size of detection models is limited, a sliding window algorithm is used to separate high-resolution map images. The final detected strike features are provided as a digital dataset that can be used for further manipulations. Thus, Convolutional Neural Network (CNN) based object detection along with a sliding window protocol can be applied to manual map digitization processes to provide instantaneous digitized data with higher accuracy. This automated process can be used to detect small features and digitize other high-resolution drawings.

Automatic vectorization of point symbols on archive maps using deep convolutional neural network

Abstract. Archive topographical maps are a key source of geographical information from past ages, which can be valuable for several science fields. Since manual digitization is usually slow and takes much human resource, automatic methods are preferred, such as deep learning algorithms. Although automatic vectorization is a common problem, there have been few approaches regarding point symbols. In this paper, a point symbol vectorization method is proposed, which was tested on Third Military Survey map sheets using a Mask Regional Convolutional Neural Network (MRCNN). The MRCNN implementation uses the ResNet101 network improved with the Feature Pyramid Network architecture and is developed in a Google Colab environment. The pretrained network was trained on four point symbol categories simultaneously. Results show 90% accuracy, while 94% of symbols detected for some categories on the complete test sheet.

AutoCirque: An automated method to delineate glacial cirque outlines from digital elevation models

Cirques are typical erosional landforms of glaciers and have been used as bases for paleoclimate and paleoenvironment reconstruction and for understanding the interactions between glacial erosion, climate, and topography. The availability of high-resolution digital elevation models (DEMs) provides the opportunity to map large populations of cirques for regional and global scale analysis. However, cirque outlines are still mainly determined based on manual digitization, which is time consuming and labor intensive. This paper introduces an automated method to recognize and delineate cirques using DEMs based on a series of hydrological and morphological analyses, including delineating stream network, filtering streams, determining potential cirque threshold points, and delineating cirque outlines. A semi-automated tool is also developed based on user-specified threshold points or cross sections. The related tools are coded in python and imported into ArcGIS as a toolbox, AutoCirque, with user friendly interfaces. Comparison in a test area of the eastern Tian Shan, China, indicated that the population statistics are relatively consistent between manually digitized and auto-delineated cirques. Detailed comparisons for 11 selected cirques indicated that the AutoCirque-delineated and manually digitized cirque outlines are similar in shape with an average boundary offset of approximately one DEM cell size (30 m) and a 70–90% overlap-fit percentage. The derived cirque metrics are also similar, especially for elevation, slope, and aspect related metrics. This toolbox can significantly speed up the analytical processes, remove the subjectivity in delineating cirque outlines, and allow for the comparison of cirque morphology and metrics at regional and global scales.

SEMI-AUTOMATIC GENERATION OF AN LOD1 AND LOD2 3D CITY MODEL OF TANAUAN CITY, BATANGAS USING OPENSTREETMAP AND TAAL OPEN LIDAR DATA IN QGIS

Abstract. 3D city models have found purpose beyond simple visualization of space by serving as building blocks of digital twins and smart cities. These are useful to urban areas in the Philippines through diversified applications: urban planning, disaster mitigation, environmental monitoring, and policy making. This study explored the use of free and open-source software to generate an LOD1 and LOD2 3D city model of Tanauan City, Batangas using building footprints from OpenStreetMap and elevation models from Taal Open LiDAR data. The proposed approach consists of GIS-based methods including data pre-processing, building height extraction, roof identification, building reconstruction, and visualization. The study adopted methods from previous studies for building detection and from Zheng et al. (2017) for LOD2 building reconstruction. For LOD1, a decision tree classifier was devised to determine the appropriate height for building extrusion. For LOD2, a model-driven approach using multipatch surfaces was utilized for building reconstruction. The workflow was able to reconstruct 70.66% LOD1 building models and 55.87% LOD2 building models with 44.37% overall accuracy. The RMSE and MAE between the extracted heights from the workflow and from manual digitization has an accuracy lower than 1 m which was within the standards of CityGML. The processing time in test bench 1 and test bench 2 for LOD1 took 00:12:54.5 and 00:09:27.2 while LOD2 took 02:50:29.1 and 01:27:48.2, respectively. The results suggest that the efficient generation of LOD1 and LOD2 3D city models from open data can be achieved in the FOSS environment using less computationally intensive GIS-based algorithms.

Mapping Relict Charcoal Hearths in New England Using Deep Convolutional Neural Networks and LiDAR Data

Advanced deep learning methods combined with regional, open access, airborne Light Detection and Ranging (LiDAR) data have great potential to study the spatial extent of historic land use features preserved under the forest canopy throughout New England, a region in the northeastern United States. Mapping anthropogenic features plays a key role in understanding historic land use dynamics during the 17th to early 20th centuries, however previous studies have primarily used manual or semi-automated digitization methods, which are time consuming for broad-scale mapping. This study applies fully-automated deep convolutional neural networks (i.e., U-Net) with LiDAR derivatives to identify relict charcoal hearths (RCHs), a type of historical land use feature. Results show that slope, hillshade, and Visualization for Archaeological Topography (VAT) rasters work well in six localized test regions (spatial scale: &lt;1.5 km2, best F1 score: 95.5%), but also at broader extents at the town level (spatial scale: 493 km2, best F1 score: 86%). The model performed best in areas with deciduous forest and high slope terrain (e.g., &gt;15 degrees) (F1 score: 86.8%) compared to coniferous forest and low slope terrain (e.g., &lt;15 degrees) (F1 score: 70.1%). Overall, our results contribute to current methodological discussions regarding automated extraction of historical cultural features using deep learning and LiDAR.

Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling

Abstract. Wind turbines in northern Europe are frequently placed in forests, which sets new wind resource modelling requirements. Accurate mapping of the land surface can be challenging at forested sites due to sudden transitions between patches with very different aerodynamic properties, e.g. tall trees, clearings, and lakes. Tree growth and deforestation can lead to temporal changes of the forest. Global or pan-European land cover data sets fail to resolve these forest properties, aerial lidar campaigns are costly and infrequent, and manual digitization is labour-intensive and subjective. Here, we investigate the potential of using satellite observations to characterize the land surface in connection with wind energy flow modelling using the Wind Atlas Analysis and Application Program (WAsP). Collocated maps of the land cover, tree height, and leaf area index (LAI) are generated based on observations from the Sentinel-1 and Sentinel-2 missions combined with the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). Three different forest canopy models are applied to convert these maps to roughness lengths and displacement heights. We introduce new functionalities for WAsP, which can process detailed land cover maps containing both roughness lengths and displacement heights. Validation is carried out through cross-prediction analyses at eight well-instrumented sites in various landscapes where measurements at one mast are used to predict wind resources at another nearby mast. The use of novel satellite-based input maps in combination with a canopy model leads to lower cross-prediction errors of the wind power density (rms = 10.9 %–11.2 %) than using standard global or pan-European land cover data sets for land surface parameterization (rms = 14.2 %–19.7 %). Differences in the cross-predictions resulting from the three different canopy models are minor. The satellite-based maps show cross-prediction errors close to those obtained from aerial lidar scans and manually digitized maps. The results demonstrate the value of using detailed satellite-based land cover maps for micro-scale flow modelling.