A5087175500- Human Mobility and Location-Based Analysis
- Urban Transport and Accessibility
- Land Use and Ecosystem Services
- Environmental Sustainability and Technology
- Advanced Research in Systems and Signal Processing
- Economic and Technological Systems Analysis
- Impact of Light on Environment and Health
- Data-Driven Disease Surveillance
- Transportation Planning and Optimization
- Geographic Information Systems Studies
- Spatial and Panel Data Analysis
- Vehicle emissions and performance
- Soil Geostatistics and Mapping
- COVID-19 epidemiological studies
- Semantic Web and Ontologies
- Energy, Environment, and Transportation Policies
- Neural Networks and Applications
- Forest Ecology and Biodiversity Studies
- Solid State Laser Technologies
- demographic modeling and climate adaptation
- Data Management and Algorithms
- Constraint Satisfaction and Optimization
- Graphene and Nanomaterials Applications
- Forest ecology and management
- Remote Sensing and LiDAR Applications
Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute
2022-2025
Chongqing University of Education
2024
Peking University
2018-2023
Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen
2022
University of Illinois Urbana-Champaign
2020
Changchun University of Science and Technology
2015
Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given sample data defined on point supports. However, continuity and heterogeneity underlying spatial are too complex to be approximated by classic statistical models. Deep learning models, especially idea conditional generative adversarial networks (CGANs), provide us with perspective for formalizing as task. In this article, we design novel deep architecture...
Inferring the unknown properties of a place relies on both its observed attributes and characteristics places to which it is connected. Because are unstructured metrics for connections can be diverse, challenging incorporate them in spatial prediction task where results could affected by how neighborhoods delineated true relevance among hard identify. To bridge gap, we introduce graph convolutional neural networks (GCNNs) model as graph, each formalized node, encoded node features,...
Mixed use has been extensively applied as an urban planning principle and hinders the study of single functions. To address this problem, it is worth decomposing mixed use. Inspired by concept spectral unmixing in remote sensing applications, paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-use terms human activities differs from traditional land research, more reasonable to infer actual function land. The consists four steps, namely temporal activity...
The spatial concentration of the human activity is a crucial indication socioeconomic vitality. Accurately mapping volumes fundamental to support regional sustainable development. Current approaches rely on mobile positioning data, which record information about daily but are inaccessible in most cities due privacy and data sharing concerns. Alternative methods needed provide more generalized predictions extensive areas while maintaining low cost. This study demonstrates how remote sensing...
Abstract Methods from artificial intelligence (AI) and, in particular, machine learning and deep learning, have advanced rapidly recent years been applied to multiple fields including geospatial analysis. Due the spatial heterogeneity fact that conventional methods can not mine large data, studies typically model homogeneous regions locally within entire study area. However, AI models process amounts of theoretically, more diverse train robust a well-trained will be. In this paper, we...
Big geo-data are often aggregated according to spatio-temporal units for analyzing human activities and urban environments. Many applications categorize such data into groups compare the characteristics across groups. The intergroup differences vary with units, essential is identify apparently different characteristics. However, dependence, variety, complexity of tasks impede an effective unit assessment. Inspired by extract critical image components based on explainable artificial...
In forestry studies, deep learning models have achieved excellent performance in many application scenarios (e.g., detecting forest damage). However, the unclear model decisions (i.e., black-box) undermine credibility of results and hinder their practicality. This study intends to obtain explanations such through use explainable artificial intelligence methods, then feature unlearning methods improve performance, which is first attempt field forestry. Results three experiments show that...
Spatial scale is a fundamental issue for geographical phenomena because the size of spatial unit adopted analysis can have significant effect on aggregated data and corresponding analytical results. There exists much research distribution while few has paid attention to interactions. Big geo-data with micro-level individual movements provide an unprecedented opportunity explore interactions from bottom up understand perspective interaction patterns. In this paper, we introduced empirical...
Precise distinction of mixed functions on urban land is essential for studies and planning, while existing methods are limited by high sampling bias, low observation frequency, lack semantic information in common data sources. In this paper, we introduce a new proxy human behavior, the telecom traffic as remedy to above limitations, present an analytical framework which utilizes anonymized aggregated infer at spatiotemporal granularities fine buildings hours. A time-series decomposition...
New models are emerging from Artificial Intelligence (AI) and its sub-fields, in particular, Machine Learning Deep that being applied different application areas including geography (e.g., land cover identification traffic volume forecasting based on spatial data). Different well-known datasets often used to develop AI ImageNet for image classification), data has an intrinsic feature, i.e., heterogeneity, which leads varying relationships across regions between the independent (i.e., model...
Nd: YLF polycrystalline raw materials were synthezed by a dry method and Nd:YLF laser crystal was grown IF indu ction Cz method.Its process parameters these:a pulling rate of 1 mm/h, the rotation speed 15 r/min 10 - 2 Pa degree vacuum.The absorption fluorescence spect ra indicates that has strong sbs orptions around 808nm at room temperature which belong t o commercial diode band under la ser pumped, emission peaks crys tal are located 1050and 1300nm ( 4 F 3/2 → I 11/2 ) have stronge r emission.
This study estimates causal effects of traffic congestion on air quality in local areas within a city, highlighting spatial variations traffic, pollution, and health risks to residents due pollutant exposure. We employ large size taxi driving trajectory data construct measures surrounding monitoring stations Beijing, at hourly basis. The station-by-hour panel enables estimation both contemporaneous lagged marginal pollution. find that increases concentrations NO2 CO contemporaneously, but...