scholarly journals Geomorphometric Methods for Burial Mound Recognition and Extraction from High-Resolution LiDAR DEMs

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1192 ◽  
Author(s):  
Mihai Niculiță
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Latin Hypercube Sampling ◽  
Current Method ◽  
Training Dataset ◽  
Great Promise ◽  
Burial Mound ◽  
Local Convexity ◽  
Full Dataset ◽  
Burial Mounds

Archaeological topography identification from high-resolution DEMs (Digital Elevation Models) is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology through which burial mounds (tumuli) from LiDAR (Light Detection And Ranging) DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further fed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and select the proper training dataset, settings, and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped, and field checked burial mounds from two neighbor study areas of 100 km2 each. The results show that by training the Random Forest on a dataset composed of between 75% and 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using Latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.

scholarly journals Geomorphometric Methods for Burial Mound Recognition and Extraction from High Resolution LiDAR DEMs

2020 ◽  
Author(s):  
Mihai Niculita
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Latin Hypercube Sampling ◽  
Current Method ◽  
Training Dataset ◽  
Great Promise ◽  
Burial Mound ◽  
Local Convexity ◽  
Full Dataset ◽  
Burial Mounds

Archaeological topography identification from high-resolution DEMs is a current method that is used with high success in archaeological prospecting of wide areas. I present a methodology trough which burial mounds (tumuli) from LiDAR DEMS can be identified. This methodology uses geomorphometric and statistical methods to identify with high accuracy burial mound candidates. Peaks, defined as local elevation maxima are found as a first step. In the second step, local convexity watershed segments and their seeds are compared with positions of local peaks and the peaks that correspond or have in vicinity local convexity segments seeds are selected. The local convexity segments that correspond to these selected peaks are further feed to a Random Forest algorithm together with shape descriptors and descriptive statistics of geomorphometric variables in order to build a model for the classification. Multiple approaches to tune and selected the proper training dataset, settings and variables were tested. The validation of the model was performed on the full dataset where the training was performed and on an external dataset in order to test the usability of the method for other areas in a similar geomorphological and archaeological setting. The validation was performed against manually mapped and field checked burial mounds from two neighbor study areas of 100 km2 each. The results show that by training the Random Forest on a dataset composed of between 75% to 100% of the segments corresponding to burial mounds and ten times more non-burial mounds segments selected using latin hypercube sampling, 93% of the burial mound segments from the external dataset are identified. There are 42 false positive cases that need to be checked, and there are two burial mound segments missed. The method shows great promise to be used for burial mound detection on wider areas by delineating a certain number of tumuli mounds for model training.

High Resolution Current Imaging by Direct Magnetic Field Sensing

2003 ◽  
Author(s):  
S.I. Woods ◽  
Nesco M. Lettsome ◽  
A.B. Cawthorne ◽  
L.A. Knauss ◽  
R.H. Koch
Keyword(s):  
Magnetic Field ◽  
High Resolution ◽  
Spatial Resolution ◽  
Great Promise ◽  
Magnetoresistive Sensor ◽  
Magnetic Sensitivity ◽  
Current Lines ◽  
Scanning Squid ◽  
Scanning Fiber ◽  
Ferromagnetic Probe

Abstract Two types of magnetic microscopes have been investigated for use in high resolution current mapping. The scanning fiber/SQUID microscope uses a SQUID sensor coupled to a nanoscale ferromagnetic probe, and the GMR microscope employs a nanoscale giant magnetoresistive sensor. Initial scans demonstrate that these microscopes can resolve current lines less than 10 µm apart with edge resolution of 1 µm. These types of microscopes are compared with the performance of a standard scanning SQUID microscope and with each other with respect to spatial resolution and magnetic sensitivity. Both microscopes show great promise for identifying current defects in die level devices.

scholarly journals Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

2020 ◽  
Vol 13 (1) ◽  
pp. 10
Author(s):  
Andrea Sulova ◽  
Jamal Jokar Arsanjani
Keyword(s):  
Climate Change ◽  
Machine Learning ◽  
Random Forest ◽  
Google Earth ◽  
Summer Season ◽  
Driving Factors ◽  
Machine Learning Algorithms ◽  
Classification And Regression Tree ◽  
Training Dataset ◽  
Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

scholarly journals A Downscaling–Merging Scheme for Improving Daily Spatial Precipitation Estimates Based on Random Forest and Cokriging

2021 ◽  
Vol 13 (11) ◽  
pp. 2040
Author(s):  
Xin Yan ◽  
Hua Chen ◽  
Bingru Tian ◽  
Sheng Sheng ◽  
Jinxing Wang ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Spatial Resolution ◽  
Daily Precipitation ◽  
Machine Learning Algorithms ◽  
Precipitation Data ◽  
Large Area ◽  
Tree Model ◽  
Daily Precipitation Data ◽  
Precipitation Estimates

High-spatial-resolution precipitation data are of great significance in many applications, such as ecology, hydrology, and meteorology. Acquiring high-precision and high-resolution precipitation data in a large area is still a great challenge. In this study, a downscaling–merging scheme based on random forest and cokriging is presented to solve this problem. First, the enhanced decision tree model, which is based on random forest from machine learning algorithms, is used to reduce the spatial resolution of satellite daily precipitation data to 0.01°. The downscaled satellite-based daily precipitation is then merged with gauge observations using the cokriging method. The scheme is applied to downscale the Global Precipitation Measurement Mission (GPM) daily precipitation product over the upstream part of the Hanjiang Basin. The experimental results indicate that (1) the downscaling model based on random forest can correctly spatially downscale the GPM daily precipitation data, which retains the accuracy of the original GPM data and greatly improves their spatial details; (2) the GPM precipitation data can be downscaled on the seasonal scale; and (3) the merging method based on cokriging greatly improves the accuracy of the downscaled GPM daily precipitation data. This study provides an efficient scheme for generating high-resolution and high-quality daily precipitation data in a large area.

scholarly journals CNN BASED DETECTION OF BUILDING ROOFS FROM HIGH RESOLUTION SATELLITE IMAGES

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 187-192
Author(s):  
L. Hang ◽  
G. Y. Cai
Keyword(s):  
High Resolution ◽  
Residential Building ◽  
Training Image ◽  
Training Dataset ◽  
Classification Approach ◽  
Object Based ◽  
High Resolution Satellite Images ◽  
Different Types ◽  
Model Training ◽  
Very High

Abstract. The detection and reconstruction of building have attracted more attention in the community of remote sensing and computer vision. Light detection and ranging (LiDAR) has been proved to be a good way to extract building roofs, while we have to face the problem of data shortage for most of the time. In this paper, we tried to extract the building roofs from very high resolution (VHR) images of Chinese satellite Gaofen-2 by employing convolutional neural network (CNN). It has been proved that the CNN is of a higher capability of recognizing detailed features which may not be classified out by object-based classification approach. Several major steps are concerned in this study, such as generation of training dataset, model training, image segmentation and building roofs recognition. First, urban objects such as trees, roads, squares and buildings were classified based on random forest algorithm by an object-oriented classification approach, the building regions were separated from other classes at the aid of visually interpretation and correction; Next, different types of building roofs mainly categorized by color and size information were trained using the trained CNN. Finally, the industrial and residential building roofs have been recognized individually and the results have been validated individually. The assessment results prove effectiveness of the proposed method with approximately 91% and 88% of quality rates in detection industrial and residential building roofs, respectively. Which means that the CNN approach is prospecting in detecting buildings with a very higher accuracy.

scholarly journals POLE-TOPS OF CHMYREVA MOHYLA BARROW

2019 ◽  
Vol 33 (4) ◽  
pp. 313-321
Author(s):  
Z. H. Popandopulo
Keyword(s):  
World War Ii ◽  
Burial Mound ◽  
World War ◽  
National Academy Of Sciences ◽  
River Region ◽  
The World ◽  
Long Time ◽  
History Of ◽  
Burial Mounds ◽  
Academy Of Sciences

In 1977 on the site of famous burial mound Chmyrеva Mohyla located on the northern outskirts of Velyka Bilozerka village of Zaporizhzhia region three bronze pole-tops with images of gryphons were found by local people on the plowed field. There is no evidence whether other artifacts have been found. Luckily nearby in Gunovka village the expedition of Institute of Archaeology of National Academy of Sciences of Ukraine was working under the leadership of Yu. V. Boltryk who got the founded artifacts and then sent them to Zaporizhzhia regional museum of local lore, history and economy. The history of excavations of Chmyrеva Mohyla numbers more than a century. They were started by F. A. Braun in 1898, M. I. Veselovskiy (1909—1910) continued the excavations and Yu. V. Boltryk in 1994 completed them. The burial mound has not been excavated in full because of various reasons. The destiny of finds from this barrow was tragic. A lot of artifacts among them silver vessels from the hiding-place which was revealed by M. I. Veselovskiy were lost during the World War II when the collections of Kharkiv historic museum were evacuated. Scythian bronze pole-tops as one of the most interesting categories of artifacts for a long time attracted attention of scholar world. They were classified by types and date, their significance in funeral ceremony and everyday life was searched for. The questions still remain. In this article we tried to put into scholar circulation a scanty type of pole-tops with the image of pacing gryphon on the pear-shaped little bell which is characteristic only for Steppe Dnieper river region. For today only eight of them are known and most of them are originated from of the burial mounds of high Scythian aristocracy: Tovsta Mohyla, Haimanova Mohyla, Chmyrova Mohyla. Chronologically they are slightly differed from other pole-tops both with the image of deer on pear-shaped little bells from Tovsta Mohyla, and with the image of deer on flat cone bushes from Haimanova Mohyla. The question about the place of production of such pole-tops is still opened. Probably just these types of pole-tops could be produced in one workshop but not all known variety of objects as V. A. Ilinska thought. One of the problems to be solved by researchers is searching for such workshops. But if these objects have been moulded by wax models the task becomes more complicated.

scholarly journals A High-Resolution Monitoring Approach of Canopy Urban Heat Island using Random Forest Model and Multi-platform Observations

2021 ◽  
Author(s):  
Shihan Chen ◽  
Yuanjian Yang ◽  
Fei Deng ◽  
Yanhao Zhang ◽  
Duanyang Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Urban Heat Island ◽  
Air Temperature ◽  
Anthropogenic Heat ◽  
Heat Island ◽  
Model Framework ◽  
Rapid Urbanization ◽  
Urban Heat ◽  
The City

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high spatial resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a Random Forest (RF) model. Firstly, the observed environmental parameters [e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF)] around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 °C. Then, the spatial distribution of CUHII was evaluated at 30-m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the de-creasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

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