scholarly journals Forgotten Nazi Forced Labour Camps: Arbeitslager Riese (Lower Silesia, SE Poland) and the Use of Archival Aerial Photography and Contemporary LiDAR and Ground Truth Data to Identify and Delineate Camp Areas

2020 ◽  
Vol 12 (11) ◽  
pp. 1802
Author(s):  
Aleksander Kamola ◽  
Sebastian Różycki ◽  
Paweł Bylina ◽  
Piotr Lewandowski ◽  
Adam Burakowski
Keyword(s):  
Local Community ◽  
Ground Truth ◽  
Concentration Camps ◽  
Aerial Photographs ◽  
Lidar Data ◽  
Ground Truth Data ◽  
Field Inspection ◽  
Se Poland ◽  
National Archives ◽  
Forced Labour

The “Riese” project was a huge construction project initiated by German Nazi authorities, which was located in the northeast of the Sowie Mountains (Ger. Eulengebirge) in southwestern Poland. Construction of the “Riese” complex took place in 1943–1945 but was left unfinished. Due to the lack of reliable sources, the exact intended function of the Riese complex is still unknown. The construction was carried out by prisoners, mostly Jews, from the main nearby concentration camps, KL Gross-Rosen and KL Auschwitz-Birkenau. Thanks to the discovery in the National Archives (NARA, USA) of a valuable series of German aerial photographs taken in February 1945, insight into the location of labour camps was obtained. These photographs, combined with LiDAR data from the Head Office of Geodesy and Cartography (Warsaw, Poland), allowed for the effective identification and field inspection of the camps’ remains. The location and delimitation of the selected labour camps were confirmed by an analysis of the 1945 aerial photograph combined with LiDAR data. These results were supported by field inspection as well as archival testimonies of witnesses. The field inspection of the construction remains indicated intentionally faulty construction works, which deliberately reduced the durability of the buildings and made them easy to demolish. The authors believe that it is urgent to continue the research and share the results with both the scientific community and the local community. The authors also want to emphasize that this less-known aspect of Holocaust history is gradually disappearing in social and institutional memory and is losing to the commercial mythologization of the Riese object.

scholarly journals CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 339-341
Author(s):  
R. Kaczynski ◽  
A. Rylko
Keyword(s):  
Supervised Classification ◽  
Local Population ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Landsat Tm ◽  
Aerial Photographs ◽  
Radiometric Calibration ◽  
Topographic Map ◽  
Ground Truth Data ◽  
Color Composite

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.

scholarly journals COMBINING AIRBORNE OBLIQUE CAMERA AND LIDAR SENSORS: INVESTIGATION AND NEW PERSPECTIVES

2018 ◽  
Vol XLII-1 ◽  
pp. 437-444 ◽  
Author(s):  
I. Toschi ◽  
F. Remondino ◽  
R. Rothe ◽  
K. Klimek
Keyword(s):  
Ground Truth ◽  
Point Clouds ◽  
Image Sensors ◽  
Lidar Data ◽  
Imaging Data ◽  
Ground Truth Data ◽  
Dense Image ◽  
Integrated Processing ◽  
Urban Mapping ◽  
Oblique Images

<p><strong>Abstract.</strong> Hybrid sensor solutions, that feature active laser and passive image sensors on the same platform, are rapidly entering the airborne market of topographic and urban mapping, offering new opportunities for an improved quality of geo-spatial products. In this perspective, a concurrent acquisition of LiDAR data and oblique imagery, seems to have all the potential to lead the airborne (urban) mapping sector a step forward. This contribution focuses on the first commercial example of such an integrated, all-in-one mapping solution, namely the Leica CityMapper hybrid sensor. By analysing two CityMapper datasets acquired over the city of Heilbronn (Germany) and Bordeaux (France), the paper investigates potential and challenges, w.r.t. (i) number and distribution of tie points between nadir and oblique images, (ii) strategy for image aerial triangulation (AT) and accuracy achievable w.r.t ground truth data, (iii) local noise level and completeness of dense image matching (DIM) point clouds w.r.t LiDAR data. Solutions for an integrated processing of the concurrently acquired ranging and imaging data are proposed, that open new opportunities for exploiting the real potential of both data sources.</p>

scholarly journals Resolution and Accuracy of an Airborne Scanning Laser System for Beach Surveys

2013 ◽  
Vol 30 (10) ◽  
pp. 2452-2464 ◽  
Author(s):  
J. H. Middleton ◽  
C. G. Cooke ◽  
E. T. Kearney ◽  
P. J. Mumford ◽  
M. A. Mole ◽  
...  
Keyword(s):  
Large Scale ◽  
Laser System ◽  
Ground Truth ◽  
Satellite System ◽  
Airborne Lidar ◽  
Scanning Laser ◽  
Lidar Data ◽  
Ground Truth Data ◽  
Short Period ◽  
Airborne Lidar Data

Abstract Airborne scanning laser technology provides an effective method to systematically survey surface topography and changes in that topography with time. In this paper, the authors describe the capability of a rapid-response lidar system in which airborne observations are utilized to describe results from a set of surveys of Narrabeen–Collaroy Beach, Sydney, New South Wales, Australia, over a short period of time during which significant erosion and deposition of the subaerial beach occurred. The airborne lidar data were obtained using a Riegl Q240i lidar coupled with a NovAtel SPAN-CPT integrated Global Navigation Satellite System (GNSS) and inertial unit and flown at various altitudes. A set of the airborne lidar data is compared with ground-truth data acquired from the beach using a GNSS/real-time kinematic (RTK) system mounted on an all-terrain vehicle. The comparison shows consistency between systems, with the airborne lidar data being less than 0.02 m different from the ground-truth data when four surveys are undertaken, provided a method of removing outliers—developed here and designated as “weaving”—is used. The combination of airborne lidar data with ground-truth data provides an excellent method of obtaining high-quality topographic data. Using the results from this analysis, it is shown that airborne lidar data alone produce results that can be used for ongoing large-scale surveys of beaches with reliable accuracy, and that the enhanced accuracy resulting from multiple airborne surveys can be assessed quantitatively.

scholarly journals Forest Road Detection Using LiDAR Data and Hybrid Classification

2021 ◽  
Vol 13 (3) ◽  
pp. 393
Author(s):  
Sandra Buján ◽  
Juan Guerra-Hernández ◽  
Eduardo González-Ferreiro ◽  
David Miranda
Keyword(s):  
Forest Management ◽  
Sustainable Forest Management ◽  
Decision Rules ◽  
Ground Truth ◽  
Rural Landscape ◽  
Lidar Data ◽  
Ground Truth Data ◽  
Forest Road ◽  
Point Density ◽  
Hybrid Classification

Knowledge about forest road networks is essential for sustainable forest management and fire management. The aim of this study was to assess the accuracy of a new hierarchical-hybrid classification tool (HyClass) for mapping paved and unpaved forest roads with LiDAR data. Bare-earth and low-lying vegetation were also identified. For this purpose, a rural landscape (area 70 ha) in northwestern Spain was selected for study, and a road network map was extracted from the cadastral maps as the ground truth data. The HyClass tool is based on a decision tree which integrates segmentation processes at local scale with decision rules. The proposed approach yielded an overall accuracy (OA) of 96.5%, with a confidence interval (CI) of 94.0–97.6%, representing an improvement over pixel-based classification (OA = 87.0%, CI = 83.7–89.8%) using Random Forest (RF). In addition, with the HyClass tool, the classification precision varied significantly after reducing the original point density from 8.7 to 1 point/m2. The proposed method can provide accurate road mapping to support forest management as an alternative to pixel-based RF classification when the LiDAR point density is higher than 1 point/m2.

scholarly journals Real-Time Queue Length Detection with Roadside LiDAR Data

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2342 ◽  
Author(s):  
Jianqing Wu ◽  
Hao Xu ◽  
Yongsheng Zhang ◽  
Yuan Tian ◽  
Xiuguang Song
Keyword(s):  
Real Time ◽  
Queue Length ◽  
Ground Truth ◽  
Lidar Data ◽  
Ground Truth Data ◽  
The Road ◽  
Detailed Method ◽  
Important Input ◽  
Novel Method ◽  
On The Road

Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed.

scholarly journals Individual Building Rooftop and Tree Crown Segmentation from High-Resolution Urban Aerial Optical Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Jichao Jiao ◽  
Zhongliang Deng
Keyword(s):  
Cost Function ◽  
Tree Height ◽  
Salient Feature ◽  
Ground Truth ◽  
Aerial Photographs ◽  
Ground Truth Data ◽  
Optical Images ◽  
Trained Classifier ◽  
The Cost ◽  
Good Agreement

We segment buildings and trees from aerial photographs by using superpixels, and we estimate the tree’s parameters by using a cost function proposed in this paper. A method based on image complexity is proposed to refine superpixels boundaries. In order to classify buildings from ground and classify trees from grass, the salient feature vectors that include colors, Features from Accelerated Segment Test (FAST) corners, and Gabor edges are extracted from refined superpixels. The vectors are used to train the classifier based on Naive Bayes classifier. The trained classifier is used to classify refined superpixels as object or nonobject. The properties of a tree, including its locations and radius, are estimated by minimizing the cost function. The shadow is used to calculate the tree height using sun angle and the time when the image was taken. Our segmentation algorithm is compared with other two state-of-the-art segmentation algorithms, and the tree parameters obtained in this paper are compared to the ground truth data. Experiments show that the proposed method can segment trees and buildings appropriately, yielding higher precision and better recall rates, and the tree parameters are in good agreement with the ground truth data.

scholarly journals CHANGE DETECTION OF LAKE ABA SAMUEL IN ETHIOPIA

2016 ◽  
Vol XLI-B8 ◽  
pp. 339-341
Author(s):  
R. Kaczynski ◽  
A. Rylko
Keyword(s):  
Supervised Classification ◽  
Local Population ◽  
Atmospheric Correction ◽  
Ground Truth ◽  
Landsat Tm ◽  
Aerial Photographs ◽  
Radiometric Calibration ◽  
Topographic Map ◽  
Ground Truth Data ◽  
Color Composite

Old topographic map published in 1975 elaborated from aerial photographs taken in 1972, Landsat TM data acquired in May 1986 and Landsat ETM+ from June 2002 have been used to assess the changes of the lake Aba Samuel in Ethiopia. First map of the lake has been done in the framework of UNDP project running in 1988-90 in the Ethiopian Mapping Authority. The second classification map has been done as M.Sc. thesis in the MUT in 2015. Supervised classification methods with the use of ground truth data have been used for elaboration of the Landsat TM data. From the year 1972 up to 1986 the area of the lake has decreased by 23%. From 1986 up to 2002 the area of the lake has decreased by 20%. Therefore, after 30 years the lake was smaller by 43%. This have had very bad influence on the lives of the local population. From other recent data in the period from 2002-2015 the lake has practically disappeared and now it is only a small part of the river Akaki. ENVI 5.2 and ERDAS IMAGINE 9.2 have been used for Radiometric Calibration, Quick Atmospheric Correction (QUAC) and supervised classification of Landsat ETM+ data. The Optimum Index Factor shows the best combination of Landsat TM and ETM+ bands for color composite as 1,4,5 in the color filters: B, G, R for the signature development. Methodology and final maps are enclosed in the paper.

scholarly journals Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

2021 ◽  
Vol 13 (10) ◽  
pp. 1966
Author(s):  
Christopher W Smith ◽  
Santosh K Panda ◽  
Uma S Bhatt ◽  
Franz J Meyer ◽  
Anushree Badola ◽  
...  
Keyword(s):  
Boreal Forest ◽  
Ground Truth ◽  
Burn Severity ◽  
Classification Methods ◽  
Spectral Indices ◽  
Ground Truth Data ◽  
Burn Scar ◽  
Interior Alaska ◽  
Remote Sensing Methods ◽  
The Relationship

In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent.

scholarly journals Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision

2020 ◽  
Vol 13 (1) ◽  
pp. 26
Author(s):  
Wen-Hao Su ◽  
Jiajing Zhang ◽  
Ce Yang ◽  
Rae Page ◽  
Tamas Szinyei ◽  
...  
Keyword(s):  
Fusarium Head Blight ◽  
Ground Truth ◽  
Wheat Breeding ◽  
Head Blight ◽  
Detection Rates ◽  
Ground Truth Data ◽  
Resistant Cultivars ◽  
Feature Pyramid ◽  
Rater Error ◽  
Wheat Lines

In many regions of the world, wheat is vulnerable to severe yield and quality losses from the fungus disease of Fusarium head blight (FHB). The development of resistant cultivars is one means of ameliorating the devastating effects of this disease, but the breeding process requires the evaluation of hundreds of lines each year for reaction to the disease. These field evaluations are laborious, expensive, time-consuming, and are prone to rater error. A phenotyping cart that can quickly capture images of the spikes of wheat lines and their level of FHB infection would greatly benefit wheat breeding programs. In this study, mask region convolutional neural network (Mask-RCNN) allowed for reliable identification of the symptom location and the disease severity of wheat spikes. Within a wheat line planted in the field, color images of individual wheat spikes and their corresponding diseased areas were labeled and segmented into sub-images. Images with annotated spikes and sub-images of individual spikes with labeled diseased areas were used as ground truth data to train Mask-RCNN models for automatic image segmentation of wheat spikes and FHB diseased areas, respectively. The feature pyramid network (FPN) based on ResNet-101 network was used as the backbone of Mask-RCNN for constructing the feature pyramid and extracting features. After generating mask images of wheat spikes from full-size images, Mask-RCNN was performed to predict diseased areas on each individual spike. This protocol enabled the rapid recognition of wheat spikes and diseased areas with the detection rates of 77.76% and 98.81%, respectively. The prediction accuracy of 77.19% was achieved by calculating the ratio of the wheat FHB severity value of prediction over ground truth. This study demonstrates the feasibility of rapidly determining levels of FHB in wheat spikes, which will greatly facilitate the breeding of resistant cultivars.

scholarly journals Classification of Cattle Behaviours Using Neck-Mounted Accelerometer-Equipped Collars and Convolutional Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4050
Author(s):  
Dejan Pavlovic ◽  
Christopher Davison ◽  
Andrew Hamilton ◽  
Oskar Marko ◽  
Robert Atkinson ◽  
...  
Keyword(s):  
Neural Network ◽  
Model Performance ◽  
Ground Truth ◽  
Practical Implementation ◽  
Ground Truth Data ◽  
Battery Lifetime ◽  
Implementation Challenges ◽  
Memory Footprint ◽  
Commercial Farms ◽  
Using Data

Monitoring cattle behaviour is core to the early detection of health and welfare issues and to optimise the fertility of large herds. Accelerometer-based sensor systems that provide activity profiles are now used extensively on commercial farms and have evolved to identify behaviours such as the time spent ruminating and eating at an individual animal level. Acquiring this information at scale is central to informing on-farm management decisions. The paper presents the development of a Convolutional Neural Network (CNN) that classifies cattle behavioural states (`rumination’, `eating’ and `other’) using data generated from neck-mounted accelerometer collars. During three farm trials in the United Kingdom (Easter Howgate Farm, Edinburgh, UK), 18 steers were monitored to provide raw acceleration measurements, with ground truth data provided by muzzle-mounted pressure sensor halters. A range of neural network architectures are explored and rigorous hyper-parameter searches are performed to optimise the network. The computational complexity and memory footprint of CNN models are not readily compatible with deployment on low-power processors which are both memory and energy constrained. Thus, progressive reductions of the CNN were executed with minimal loss of performance in order to address the practical implementation challenges, defining the trade-off between model performance versus computation complexity and memory footprint to permit deployment on micro-controller architectures. The proposed methodology achieves a compression of 14.30 compared to the unpruned architecture but is nevertheless able to accurately classify cattle behaviours with an overall F1 score of 0.82 for both FP32 and FP16 precision while achieving a reasonable battery lifetime in excess of 5.7 years.

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