Demystifying the Differences between Structure-from-Motion Software Packages for Pre-Processing Drone Data

With the increased availability of low-cost, off-the-shelf drone platforms, drone data become easy to capture and are now a key component of environmental assessments and monitoring. Once the data are collected, there are many structure-from-motion (SfM) photogrammetry software options available to pre-process the data into digital elevation models (DEMs) and orthomosaics for further environmental analysis. However, not all software packages are created equal, nor are their outputs. Here, we evaluated the workflows and output products of four desktop SfM packages (AgiSoft Metashape, Correlator3D, Pix4Dmapper, WebODM), across five input datasets representing various ecosystems. We considered the processing times, output file characteristics, colour representation of orthomosaics, geographic shift, visual artefacts, and digital surface model (DSM) elevation values. No single software package was determined the “winner” across all metrics, but we hope our results help others demystify the differences between the options, allowing users to make an informed decision about which software and parameters to select for their specific application. Our comparisons highlight some of the challenges that may arise when comparing datasets that have been processed using different parameters and different software packages, thus demonstrating a need to provide metadata associated with processing workflows.

Logging Trail Segmentation via a Novel U-Net Convolutional Neural Network and High-Density Laser Scanning Data

Logging trails are one of the main components of modern forestry. However, spotting the accurate locations of old logging trails through common approaches is challenging and time consuming. This study was established to develop an approach, using cutting-edge deep-learning convolutional neural networks and high-density laser scanning data, to detect logging trails in different stages of commercial thinning, in Southern Finland. We constructed a U-Net architecture, consisting of encoder and decoder paths with several convolutional layers, pooling and non-linear operations. The canopy height model (CHM), digital surface model (DSM), and digital elevation models (DEMs) were derived from the laser scanning data and were used as image datasets for training the model. The labeled dataset for the logging trails was generated from different references as well. Three forest areas were selected to test the efficiency of the algorithm that was developed for detecting logging trails. We designed 21 routes, including 390 samples of the logging trails and non-logging trails, covering all logging trails inside the stands. The results indicated that the trained U-Net using DSM (k = 0.846 and IoU = 0.867) shows superior performance over the trained model using CHM (k = 0.734 and IoU = 0.782), DEMavg (k = 0.542 and IoU = 0.667), and DEMmin (k = 0.136 and IoU = 0.155) in distinguishing logging trails from non-logging trails. Although the efficiency of the developed approach in young and mature stands that had undergone the commercial thinning is approximately perfect, it needs to be improved in old stands that have not received the second or third commercial thinning.

Accuracy Assessment of Post-processing Kinematic Georeferencing Based on Real-time Kinematic Unmanned Aerial Vehicle and Structure-from-motion Photogrammetry: Topographic Measurements of a Riverbed in a Small Watershed with a Check Dam

<p><a>To obtain an accurate digital surface model of the small watershed topography of a forested area while reducing time and labor costs, we used a consumer-grade unmanned aerial vehicle (UAV) with a build-in real-time kinematic global navigation satellite system. The applicability of structure-from-motion (SfM) multi-view stereo processing with post-processing kinematic (PPK) correction of the positional coordinate data (the UAV-PPK-SfM method) was tested. Nine verification points were set up in a small (0.5 km²) watershed, based on a check dam in the headwaters of a forest area. The location information of the verification points extracted from the digital surface model acquired by UAV-PPK-SfM and the overall working time were compared with the corresponding location information and working time of a traditional field survey using a total station. The results showed that the vertical error between the total station and each verification point at an altitude of 150 m ranged from 0.006 to 0.181 m. The working time of the UAV-PK-SfM survey was 10 % of that of the total station survey (30 min). The UAV-PPK-SfM workflow proposed in this study shows that wide-area, non-destructive topographic surveying, including fluvial geomorphological mapping, is possible with a vertical error of ±0.2 m in small watersheds (<0.5 km²). This method will be useful for rapid topographic surveying in inaccessible areas during disasters, such as monitoring debris flow at check dam sites, and for efficient topographic mapping of steep valleys in forested areas where the positioning of ground control points is a laborious task.</a></p>

Accuracy Assessment of Post-processing Kinematic Georeferencing Based on Real-time Kinematic Unmanned Aerial Vehicle and Structure-from-motion Photogrammetry: Topographic Measurements of a Riverbed in a Small Watershed with a Check Dam

<p><a>To obtain an accurate digital surface model of the small watershed topography of a forested area while reducing time and labor costs, we used a consumer-grade unmanned aerial vehicle (UAV) with a build-in real-time kinematic global navigation satellite system. The applicability of structure-from-motion (SfM) multi-view stereo processing with post-processing kinematic (PPK) correction of the positional coordinate data (the UAV-PPK-SfM method) was tested. Nine verification points were set up in a small (0.5 km²) watershed, based on a check dam in the headwaters of a forest area. The location information of the verification points extracted from the digital surface model acquired by UAV-PPK-SfM and the overall working time were compared with the corresponding location information and working time of a traditional field survey using a total station. The results showed that the vertical error between the total station and each verification point at an altitude of 150 m ranged from 0.006 to 0.181 m. The working time of the UAV-PK-SfM survey was 10 % of that of the total station survey (30 min). The UAV-PPK-SfM workflow proposed in this study shows that wide-area, non-destructive topographic surveying, including fluvial geomorphological mapping, is possible with a vertical error of ±0.2 m in small watersheds (<0.5 km²). This method will be useful for rapid topographic surveying in inaccessible areas during disasters, such as monitoring debris flow at check dam sites, and for efficient topographic mapping of steep valleys in forested areas where the positioning of ground control points is a laborious task.</a></p>

Comparison of Digital Terrain and Surface Models for next Usage in a Chosen Locality

There are many possibilities for applications of digital terrain model and digital surface model due to their georeferenced character. The informational system of georeferenced data of Slovakia called ZBGIS gives new opportunities of downloading digital data in various formats. It is possible to download ortophotomosaics, ZBGIS raster at various scales, point cloud but digital terrain models and digital surface models with great possibilities of their application in GIS calculations as well.

Comparing Three Machine Learning Techniques for Building Extraction from a Digital Surface Model

Automatic building extraction from high-resolution remotely sensed data is a major area of interest for an extensive range of fields (e.g., urban planning, environmental risk management) but challenging due to urban morphology complexity. Among the different methods proposed, the approaches based on supervised machine learning (ML) achieve the best results. This paper aims to investigate building footprint extraction using only high-resolution raster digital surface model (DSM) data by comparing the performance of three different popular supervised ML models on a benchmark dataset. The first two methods rely on a histogram of oriented gradients (HOG) feature descriptor and a classical ML (support vector machine (SVM)) or a shallow neural network (extreme learning machine (ELM)) classifier, and the third model is a fully convolutional network (FCN) based on deep learning with transfer learning. Used data were obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) and cover the urban areas of Vaihingen an der Enz, Potsdam, and Toronto. The results indicated that performances of models based on shallow ML (feature extraction and classifier training) are affected by the urban context investigated (F1 scores from 0.49 to 0.81), whereas the FCN-based model proved to be the most robust and best-performing method for building extraction from a high-resolution raster DSM (F1 scores from 0.80 to 0.86).

BUILDING OUTLINE EXTRACTION FROM AERIAL IMAGERY AND DIGITAL SURFACE MODEL WITH A FRAME FIELD LEARNING FRAMEWORK

Deep learning-based semantic segmentation models for building delineation face the challenge of producing precise and regular building outlines. Recently, a building delineation method based on frame field learning was proposed by Girard et al. (2020) to extract regular building footprints as vector polygons directly from aerial RGB images. A fully convolution network (FCN) is trained to learn simultaneously the building mask, contours, and frame field followed by a polygonization method. With the direction information of the building contours stored in the frame field, the polygonization algorithm produces regular outlines accurately detecting edges and corners. This paper investigated the contribution of elevation data from the normalized digital surface model (nDSM) to extract accurate and regular building polygons. The 3D information provided by the nDSM overcomes the aerial images’ limitations and contributes to distinguishing the buildings from the background more accurately. Experiments conducted in Enschede, the Netherlands, demonstrate that the nDSM improves building outlines’ accuracy, resulting in better-aligned building polygons and prevents false positives. The investigated deep learning approach (fusing RGB + nDSM) results in a mean intersection over union (IOU) of 0.70 in the urban area. The baseline method (using RGB only) results in an IOU of 0.58 in the same area. A qualitative analysis of the results shows that the investigated model predicts more precise and regular polygons for large and complex structures.