A new explainable DTM generation algorithm with airborne LIDAR data: grounds are smoothly connected eventually

The digital terrain model (DTM) is fundamental geospatial data for various studies in urban, environmental, and Earth science. The reliability of the results obtained from such studies can be considerably affected by the errors and uncertainties of the underlying DTM. Numerous algorithms have been developed to mitigate the errors and uncertainties of DTM. However, most algorithms involve tricky parameter selection and complicated procedures that make the algorithm's decision rule obscure, so it is often difficult to explain and predict the errors and uncertainties of the resulting DTM. Also, previous algorithms often consider the local neighborhood of each point for distinguishing non-ground objects, which limits both search radius and contextual understanding and can be susceptible to errors particularly if point density varies. This study presents an open-source DTM generation algorithm for airborne LiDAR data that can consider beyond the local neighborhood and whose results are easily explainable, predictable, and reliable. The key assumption of the algorithm is that grounds are smoothly connected while non-grounds are surrounded by areas having sharp elevation changes. The robustness and uniqueness of the proposed algorithm were evaluated in geographically complex environments through tiling evaluation compared to other state-of-the-art algorithms

Scalable Surface Water Mapping up to Fine-scale using Geometric Features of Water from Topographic Airborne LiDAR Data

Despite substantial technological advancements, the comprehensive mapping of surface water, particularly smaller bodies (<1ha), continues to be a challenge due to a lack of robust, scalable methods. Standard methods require either training labels or site-specific parameter tuning, which complicates automated mapping and introduces biases related to training data and parameters. The reliance on water's reflectance properties, including LiDAR intensity, further complicates the matter, as higher-resolution images inherently produce more noise. To mitigate these difficulties, we propose a unique method that focuses on the geometric characteristics of water instead of its variable reflectance properties. Unlike preceding approaches, our approach relies entirely on 3D coordinate observations from airborne LiDAR data, taking advantage of the principle that connected surface water remains flat due to gravity. By harnessing this natural law in conjunction with connectivity, our method can accurately and scalably identify small water bodies, eliminating the need for training labels or repetitive parameter tuning. Consequently, our approach enables the creation of comprehensive 3D topographic maps that include both water and terrain, all performed in an unsupervised manner using only airborne laser scanning data, potentially enhancing the process of generating reliable 3D topographic maps. We validated our method across extensive and diverse landscapes, while comparing it to highly competitive Normalized Difference Water Index (NDWI)-based methods and assessing it using a reference surface water map. In conclusion, our method offers a new approach to address persistent difficulties in robust, scalable surface water mapping and 3D topographic mapping, using solely airborne LiDAR data

Assessment of Local Climate Zone Products via Simplified Classification Rule with 3D Building Maps

This study assesses the performance of a global Local Climate Zone (LCZ) product. We examined the built-type classes of LCZs in three major metropolitan areas within the U.S. A reference LCZ was constructed using a simple rule-based method based on high-resolution 3D building maps. Our evaluation demonstrated that the global LCZ product struggles to differentiate classes that demand precise building footprint information (Classes 6 and 9), and classes that necessitate the identification of subtle differences in building elevation (Classes 4-6). Additionally, we identified inconsistent tendencies, where the distribution of classes skews differently across different cities, suggesting the presence of a data distribution shift problem in the machine learning-based LCZ classifier. Our findings shed light on the uncertainties in global LCZ maps, help identify the LCZ classes that are the most challenging to distinguish, and offer insight into future plans for LCZ development and validation

Geospatial Clustering Analysis on Drug Abuse Emergencies

The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis

Geospatial Clustering Analysis on Drug Abuse Emergencies

The epidemic of drug abuse is a serious public health issue in the U.S. The number of overdose deaths involving prescription opioids and illicit drugs has continuously increased over the last few years. This study aims to develop a geospatial model that identifies geospatial clusters in terms of socioeconomic and demographic characteristics with an unsupervised machine learning algorithm. Then, we suggest the most important features affecting heroin overdose both negatively and positively. The findings of this study may inform policymakers about strategies to mitigate the drug overdose crisis

A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation

Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach

Spatial clustering of heroin-related overdose incidents: a case study in Cincinnati, Ohio

Drug overdose is one of the top leading causes of accidental death in the U.S., largely due to the opioid epidemic. Although the opioid epidemic is a nationwide issue, it has not affected the nation uniformly

A Library Approach to Establish an Educational Data Curation Framework (EDCF) that Supports K-12 Data Science Sustainability

It has been the tradition of the libraries to support literacy. Executive Order, Making Open and Machine Readable the New Default for Government Information, May 9, 2013, implies new roles for libraries. The library has the responsibility to support geospatial data, big data, earth science data or cyber infrastructure data that may support STEM for educational pipeline stimulation. (More information can be found at http://www.whitehouse.gov/the-press-office/2013/05/09/executive-order-making-open-and-machine-readable-new-default-government-.) Provided is an Educational Data Curation Framework (EDCF) that has been initiated in Purdue research. The EDCF may be applied to geospatial data service, engagement and outreach endeavors to augment the data science and climate literacy needs of future global citizens

How Structural Complexity of Vegetation Facilitates Invasion: Integrating LiDAR and FIA Invasive Species Plot Data in the Appalachian Mountains of the USA

This study examines how the vertical structure of forests and the variation in forest canopy tree composition relates to where forest plant invasions occur at a regional scale. We used LiDAR data on vertical structure of forests collected across 16 counties of western North Carolina, and Forest Inventory and Analysis (FIA) abundance data of invasive plant species and canopy tree species from 575 plots. We found that nearly one third of these plots were invaded by at least one invasive plant species (range = 1 to 8 species). We derived canopy gaps/clear-cut areas of the study site using LiDAR data matrix (RH100) and 2006 NLCD image to compare invasive species richness at the vegetation gap and closed canopy areas. The most frequently occurring invasive species of the 22 recorded invasive species in the vegetation gap and closed canopy areas were Lonicera japonica (Japanese honeysuckle; 67% & 49%), Rosa spp. (non-native rose; 58% & 51%) and Ligustrum sinense (Chinese privet; 36% & 25%) respectively. Majority of invasive species in both vegetation gap and closed canopy areas are dispersed by birds/ small mammals. Preliminary results suggest that plots in areas having greater forest structural complexity have less invasive plant species present. A variety of mechanisms can explain how forest structural complexity may impact invasivability. We conclude by summarizing these possible mechanisms and the role that LiDAR can play in studying and managing forested landscapes threatened by invasive species

Population pharmacokinetics of everolimus in patients with seizures associated with focal cortical dysplasia

Background: Everolimus is an inhibitor of mammalian target of rapamycin complex 1. As mutations in TSC1 and TSC2, which cause partial-onset seizures associated with TSC, were found in focal cortical dysplasia type Ⅱ (FCD Ⅱ) patients, a clinical trial has been performed to explore the efficacy and safety of everolimus in FCD patients. However, no dosage regimen was determined to treat FCD II. To recommend an optimal dose regimen for FCD patients, a population pharmacokinetic model of everolimus in FCD patients was developed.Methods: The data of everolimus were collected from September 2017 to May 2020 in a tertiary-level hospital in Korea. The model was developed using NONMEM® software version 7.4.1 (Icon Development Solutions, Ellicott City, MD, United States).Results: The population pharmacokinetics of everolimus was described as the one-compartment model with first-order absorption, with the effect of BSA on clearance. The final model was built as follows: TVCL = 12.5 + 9.71 × (BSA/1.5), TVV = 293, and TVKA = 0.585. As a result of simulation, a dose higher than 7 mg/m2 is needed in patients with BSA 0.5 m2, and a dose higher than 6 mg/m2 is needed in patients with BSA 0.7 m2. A dose of 4.5 mg/m2 is enough in the population with BSA higher than 1.5 m2 to meet the target trough range of 5–15 ng/mL.Conclusion: Based on the developed pharmacokinetics model, the optimal dose of everolimus in practice was recommended by considering the available strengths of Afinitor disperz®, 2 mg, 3 mg, and 5 mg