Zhou Huang

Peking University | PKU · Institute of Remote Sensing & GIS

Ecological footprint (EF) plays an important role in ecological and geographical analysis, but it can only be calculated based on statistical data in a region like a country or a city. High-resolution mapping of ecological footprint is in urgent need for fine-grained analysis of carbon emission and resource consumption. However, current downscaling...

Commuting flow prediction is a crucial issue for transport optimization and urban planning. However, the two existing types of solutions have inherent flaws. One is traditional models, such as the gravity model and radiation model. These models rely on fixed and simple mathematical formulas derived from physics, and ignore rich geographic semantics...

Location prediction based on spatio-temporal footprints in social media is instrumental to various applications, such as travel behavior studies, crowd detection, traffic control, and location-based service recommendation. In this study, we propose a model that uses geotags of social media to predict the potential area containing users' next locati...

Improving our comprehension of the weight and spatial distribution of urban built environment stocks is essential for informing urban resource, waste, and environmental management, but this is often hampered by inaccuracy and inconsistency of the typology and material composition data of buildings and infrastructure. Here, we have integrated big da...

Urban development is undergoing rapid growth, and the increasingly prominent environmental protection challenge underscores the crucial need to balance urban vitality development with carbon emissions for sustainable goals. how urban vitality affect carbon emissions? By collecting and analyzing data from 222 prefecture-level cities, utilizing long-...

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method th...

The United Nations has proposed Sustainable Development Goals (SDGs), of which SDG11 aims to “make cities and human settlements inclusive, safe, resilient and sustainable”. This is in line with Urban Vitality's objectives. This study proposes a quantitative framework to evaluate the impact of urban morphology on urban vitality. In this framework, a...

Active travel, namely walking and cycling, is an eco-friendly and socially beneficial mode of sustainable transportation. However, existing research on active travel relies on limited survey data and generalized linear models. To fill the gap, our study integrates large-scale big trip data and data-driven machine learning to simultaneously predict...

Illegal construction is a common problem often encountered by cities with rapid development, which is hard to deal with for multiple reasons. Although these illegal buildings are primarily defined by laws and regulations, they still have physical characteristics in common that makes them identifiable. In this study, we propose an illegal building m...

Semantic segmentation is an essential technique in remote sensing. Until recently, most related research has focused primarily on advancing semantic segmentation models based on monomodal imagery, and less attention has been given to models that utilize multimodal remote sensing data. Moreover, most current multimodal approaches consider only limit...

As the frontier of urbanization, urban-rural fringes (URFs) transitionally connect urban construction regions to the rural hinterland, and its identification is significant for the study of urbanization-related socioeconomic changes and human dynamics. Previous research on URF identification has predominantly relied on remote sensing data, which of...

Precise distinction of mixed functions on urban land is essential for urban studies and planning, while existing methods are limited by high sampling bias, low observation frequency, and lack of semantic information in common data sources. In this paper, we introduce a new proxy for human behavior, the telecom traffic data as a remedy to the above...

The fusion of spatial data with blockchain technologies presents an innovative approach towards a decentralized, secure, and trustworthy framework for spatial information management. This integration brings spatial representation to the forefront of blockchain, opening avenues for various sectorial applications. However, challenges like slow proces...

End-of-life vehicles (ELVs) present both opportunities and challenges for the environment and the economy, where effective recycling management plays a decisive role. Recently, the primary focus of recycling management has shifted from simply meeting demand to refining and optimizing processes at the city-scale. However, the mismatch in recycling c...

Anthropogenic NO\(_2\) concentrations cause climate change and human health issues. Previous studies have focused on the contribution of traffic factors to NO\(_2\) emissions but have ignored the spatially varying impact of public transport supply and demand on high-resolution NO\(_2\) concentrations. This study first applies a two-stage interpolat...

The surging accumulation of trajectory data has yielded invaluable insights into urban systems, but it has also presented challenges for data storage and management systems. In response, specialized storage systems based on non-relational databases have been developed to support large data quantities in distributed approaches. However, these system...

We present PolyBuilding, a polygon Transformer for building extraction. PolyBuilding direct predicts vector representation of buildings from remote sensing images. It builds upon an encoder-decoder transformer architecture and simultaneously predicts the bounding boxes and polygons for the building instances. Given a set of polygon queries, the mod...

Cross-city point of interest (POI) recommendation for tourists in an unfamiliar city has high application value but is challenging due to the data sparsity. Most existing models attempt to alleviate the sparsity problem by learning the user preference transfer and drift. However, they either fail to simultaneously model the preference transfer and...

Improving the travel ratio of public transportation (PTR) is important for realizing low-carbon transportation and sustainable city development. However, limited by data resolution and model accuracy, existing research rarely involves the spatially refined calculation of PTR and the quantitative analysis of its influencing factors. In this study, b...

Zero energy buildings are considered as a viable way to reduce energy use and CO2 emissions in the building sector. This study proposes a high-resolution assessment framework by combining top-down and bottom-up approaches to evaluate the zero energy potential of buildings with photovoltaic systems at the city level. We selected Shanghai as a case s...

This paper is a systematic review of the contemporary research progresses of urban vitality in China. Through examining the most related and advanced Chinese academic literatures, this paper introduces the core definitions and concepts and summarizes the methodology and findings of current Chinese urban vitality research. The data sources and quant...

Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite gr...

In recent decades, urbanization has led to an increase in building material stock. The high‐resolution quantification of building stock is essential to understand the spatial concentration of materials, urban mining potential, and sustainable urban development. Current approaches rely excessively on statistics or survey data, both of which are unav...

Topological road-boundary detection using remote sensing imagery plays a critical role in creating high-definition (HD) maps and enabling autonomous driving. Previous approaches follow an iterative graph-growing paradigm for road-boundary extraction, where road boundaries are predicted vertex by vertex and instance by instance to output a graph, re...

The geographically weighted regression (GWR) is an essential tool for estimating the spatial variation of relationships between dependent and independent variables in geographical contexts. However, GWR suffers from the problem that classical linear regressions, which compose the GWR model, are more prone to be underfitting, especially for signific...

The dockless bike-sharing (DBS) system offers a flexible feeder mode for connecting to public transport. Using multi-source big data, this study employs a multi-scale geographically weighted regression to analyze the effects of built environment characteristics on the integrated usage of DBS and public transport. Different modes of public transport...

We present PolyBuilding, a fully end-to-end polygon Transformer for building extraction. PolyBuilding direct predicts vector representation of buildings from remote sensing images. It builds upon an encoder-decoder transformer architecture and simultaneously outputs building bounding boxes and polygons. Given a set of polygon queries, the model lea...

Urban sustainability requires a coordinated development between urban built environment and human activities in cities. The irrational allocation of built environment stocks such as buildings and roads has led to urban problems like urban villages and ghost cities. However, the human use efficiency of urban built environment within cities and their...

Driving trajectory representation learning is of great significance for various location-based services such as driving pattern mining and route recommendation. However, previous representation generation approaches rarely address three challenges: (1) how to represent the intricate semantic intentions of mobility inexpensively, (2) complex and wea...

Driving trajectory representation learning is of great significance for various location-based services such as driving pattern mining and route recommendation. However, previous representation generation approaches rarely address three challenges: (1) how to represent the intricate semantic intentions of mobility inexpensively, (2) complex and wea...

The rising prosperity of Location-based Social Networks (LBSNs) witnessed an explosion in the availability of geo-tagged social media data, which enables tremendous location-aware online services, especially next point of interest (POI) recommendation. However, previous next POI recommendation studies usually adopt fix-length time windows for user...

The dynamics of human activities can illustrate how a city operates at different times. In today's highly connected urban systems, the travel flow rhythms can help uncover the functionalities of urban spaces. For efficient land use and traffic planning, it is necessary to understand how outflows and inflows of intra-urban regions change under the i...

Road traffic is an important contributor to CO2 emissions. Previous studies lack enough spatiotemporal resolution in emission calculation at the road level and ignore the impact of the built environment on road traffic emissions. Therefore, this study develops a bottom-up methodology based on the traffic trajectory data to analyze the CO2 emission...

The site selection for hybrid offshore wind and wave power plants (HOWWPP) is a critical step to a successful HOWWPP project. In this study, a four-stage framework is presented for determining the most suitable marine areas for the siting of HOWWPP. First, wind and wave energy potentials are assessed as a foundation for the implementation of a HOWW...

Congestion, whether recurrent or non-recurrent, propagates through the road network. The process of congestion propagation from a particular road to its neighbors can be regarded as a kind of message passing with a directed relationship. Existing methods have created a solid foundation for characterizing congestion propagation; however, they are ei...

Urban transit networks need to be upgraded in accordance with urban development. While methods have been studied to design an optimal transit network given the locations of stations, these methods focus on the whole network as the optimization object. However, the strategy to improve parts of an existing transit network based on the gap between tra...

Identifying urban functional zones is of great significance for understanding urban structure and urban planning. The rapid growth and open accessibility of multi-source big data, including remote sensing imagery and social sensing data, lead to a new way for dynamic identification of urban functional zones. Here, we propose an SOE (scene-object-ec...

An anomalous geographical region refers to a collection of spatially aggregated objects whose non‐spatial attribute values are significantly inconsistent with those of their spatial neighbors. The detection of anomalous regions plays an important role in spatial data mining. However, the requirement of user‐specified parameters for spatial neighbor...

Understanding quantitative relationships between urban elements is crucial for a wide range of applications. The observation at the macroscopic level demonstrates that the aggregated urban quantities (e.g., gross domestic product) scale systematically with population sizes across cities, also known as urban scaling laws. However, at the mesoscopic...

The outbreak of Corona Virus Disease 2019 (COVID-19) has affected the lives of people all over the world. It is particularly urgent and important to analyze the epidemic spreading law and support the implementation of epidemic prevention measures. It is found that there is a moderate to high correlations between the number of newly diagnosed cases...

Urban land use mapping is crucial for effective urban management and planning due to the rapid change of urban processes. State-of-the-art approaches rely heavily on the socioeconomic, topographical, infrastructural and land cover information of urban environments via feeding them into ad hoc classifiers for land use classification. Yet, the major...

The spatial concentration of the human activity is a crucial indication of socioeconomic vitality. Accurately mapping activity volumes is fundamental to support the regional sustainable development. Current approaches rely on mobile positioning data, which record information about human daily activity but are inaccessible in most cities due to priv...

Public transport performance not only directly depicts the convenience of a city’s public transport, but also indirectly reflects urban dwellers’ life quality and urban attractiveness. Understanding why some regions are easier to get around by public transport helps to build a green transport friendly city. This paper initiates a new perspective an...

City image in general refers to the perception, the feeling, and the opinion of a city, which contributes great importance to urban management, urban planning, urban cultural perceptions, and tourism resource development. Traditionally, city image is often inferred by the ‘five-element’ model of physical factors while lacking the consideration of s...

There is a Chinese proverb, “if your wine tastes really good, you do not need to worry about the location of your bar (酒香不怕巷子深)”, which implies that the popular places for local residents are sometimes hidden behind an unassuming door or on unexpected streets. Discovering these unassuming places (e.g. restaurants) of a city will benefit the underst...

Inferring the unknown properties of a place relies on both its observed attributes and the characteristics of the places to which it is connected. Because place characteristics are unstructured and the metrics for place connections can be diverse, it is challenging to incorporate them in a spatial prediction task where the results could be affected...

Understanding quantitative relationships between urban elements is crucial for a wide range of applications. The observation at the macroscopic level demonstrates that the aggregated urban quantities (e.g., gross domestic product) scale systematically with population size across cities, also known as urban scaling laws. However, at the mesoscopic l...

Nationwide migrations have drawn much attention from both geographical and social sciences. Compared to census-data-based studies, data collected from broadly used location-awareness devices enable us to describe migrant patterns with timely and fine spatial resolutions. Using a mobile positioning dataset, this paper first analyzes the spatial patt...

Mixed use has been extensively applied as an urban planning principle and hinders the study of single urban functions. To address this problem, it is worth decomposing the mixed use. Inspired by the concept of spectral unmixing in remote sensing applications, this paper proposes a framework for mixed-use decomposition based on big geo-data. Mixed-u...

In the era of big data, spatial clustering is a very important means for geo-data analysis. When clustering big geo-data such as social media check-in data, geotagged photos, and taxi trajectory points, traditional spatial clustering algorithms are facing more challenges. On the one hand, existing spatial clustering tools cannot support the cluster...

The impacts of typhoons on coastal areas around the globe necessitate risk assessments of typhoon events to analyse their paths, intensity and impacts on the environment for disaster prevention and reduction, as well as for scientific assessments of typhoon impacts. To this end, the typhoon wind field needs to be obtained by inversion to attain its...

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high l...

Geo-tagged travel photos on social networks often contain location data such as points of interest (POIs), and also users’ travel preferences. In this paper, we propose a hybrid ensemble learning method, BAyes-Knn, that predicts personalized tourist routes for travelers by mining their geographical preferences from these location-tagged data. Our m...