GIS Datasets Research Articles

Concepts Towards Nation-Wide Individual Tree Data and Virtual Forests

Individual tree data could offer potential uses for both forestry and landscape visualization but has not yet been realized on a large scale. Relying on 5 points/m2 Finnish national laser scanning, we present the design and implementation of a system for producing, storing, distributing, querying, and viewing individual tree data, both in a web browser and in a game engine-mediated interactive 3D visualization, “virtual forest”. In our experiment, 3896 km2 of airborne laser scanning point clouds were processed for individual tree detection, resulting in over 100 million trees detected, but the developed technical infrastructure allows for containing 10+ billion trees (a rough number of log-sized trees in Finland) to be visualized in the same system. About 92% of trees wider than 20 cm in diameter at breast height (corresponding to industrial log-size trees) were detected using national laser scanning data. Obtained relative RMSE for height, diameter, volume, and biomass (stored above-ground carbon) at individual tree levels were 4.5%, 16.9%, 30.2%, and 29.0%, respectively. The obtained RMSE and bias are low enough for operational forestry and add value over current area-based inventories. By combining the single-tree data with open GIS datasets, a 3D virtual forest was produced automatically. A comparison against georeferenced panoramic images was performed to assess the verisimilitude of the virtual scenes, with the best results obtained from sparse grown forests on sites with clear landmarks. Both the online viewer and 3D virtual forest can be used for improved decision-making in multifunctional forestry. Based on the work, individual tree inventory is expected to become operational in Finland in 2026 as part of the third national laser scanning program.

Assessment of wetland loss through industrialization and community expansion: a case study of the greater amanzule wetland in the Ellembelle district of Ghana

Wetland ecosystems provide numerous services to humanity and the biosphere at large. However, these services are constantly dwindling due to the massive wetland loss caused by urbanization. Urbanization-induced wetland loss is a global problem, with most developing countries experiencing it currently. Several studies on wetland loss have been conducted. However, there is a dearth of studies integrating GIS and other ecological models to estimate the periodic deterioration of wetlands in rural settings in underdeveloped nations. As a result, this study aims to determine wetland loss in a developing country’s countryside, using the Greater Amanzule Wetland in the Ellembelle District of Ghana as a case study. The study used the GIS and Remote Sensing dataset, the Normalization Difference Vegetation Index, and the InVEST-Habitat Quality model to analyze the spatial pattern of wetland loss and habitat quality variations caused by industrialization and community expansions between 2000 and 2020. Our results indicate that, from 2000 to 2015, community built-up and industrial land areas grew from 25.19 ha to 84.21 ha and 0–4.71 ha respectively, causing a wetland loss of −39.64 ha (−31.10%). Between 2015 and 2020, community built-up land grew to 78.65 ha while industrial land slightly reduced to 2.54 ha, leading to the wetland degradation of approximately −69.06 ha (−42.53%). Wetland habitat quality steadily declined, from 0.97 in 2000 to 0.93 in 2015 and 0.93 in 2020. The study findings led to policy recommendations to reduce wetland encroachment and enhance sustainability. These recommendations included conducting effective land-use plan inspections before issuing permits, coordinating effective management, and promoting the recovery of natural and artificial vegetation to sustain the wetland.

Pre-Dam Vltava River Valley—A Case Study of 3D Visualization of Large-Scale GIS Datasets in Unreal Engine

This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km2 and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems since they present different challenges and require different approaches. This article presents several relevant scientific studies and projects that have successfully used game engines for similar purposes. This case study focuses on the computational techniques used in Unreal Engine for the 3D visualization of GIS data and the potential application of Unreal Engine in large-scale geo-visualizations. It explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality for working with GIS data, such as the Vitruvio plug-in to implement procedural modeling of buildings. The case study is applied to GIS datasets of the historical Vltava Valley covering an area of 1670 km2 to demonstrate the unique challenges of using Unreal Engine to create realistic visualizations of large-scale historical landscapes. The resulting visualizations are presented. The practical application of this research provides insights into the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale historical areas.

Culture, climate, and landscape shape the savanna and wetland mosaic of the Bolivian Amazon

Environmental archeology of the Amazon has conventionally focused on terrestrial spaces, investigating crop domestication and production, agroforestry and anthropogenic soils. Aquatic environments, however, have been given less attention by archeologists and paleoenvironmental scientists alike. The Llanos de Mojos, a 135,000 km2 basin of the Amazon and mosaic landscape of savannas, wetlands and forests, has long been recognized for its archeological record. A variety of anthropogenic earthworks across the region show significant landscape modifications from Pre-Columbian times used for transportation, resource production and control of floodwaters. Here, we investigate the use and management of wetlands, and their interaction in the wider Mojos mosaic landscape, by integrating extensive archeological, paleoenvironmental and GIS datasets. We demonstrate that past human modifications of the landscape enabled the creation of complex wetland-terrestrial system of resource production, in which wetlands were managed, complementary to terrestrial crop production and agroforestry. Across short distances (40 km), land use strategies were heterogeneous despite being connected in time and space. Additionally, mixed wetland-terrestrial resource production was relatively stable during periods of lower rainfall from 1200–1500 CE, implying that this strategy provided flexibility under conditions of variable climate. The wetlands of the Llanos de Mojos are the result of millennia of human intervention and recognition of this deep heritage is required to make informed management and conservation decisions to ensure the future of this culturally and biologically diverse region.

Identification and mapping of groundwater recharge zones using multi influencing factor and analytical hierarchy process.

The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones.

Border controls and (im)mobilities: experiences from a public transport node

The Öresund Bridge between Sweden and Denmark was opened in 2000, allowing both road traffic and fast commuter trains to travel between Malmö and Copenhagen. Various agreements have long facilitated passport-free travel. However, at the end of 2015, temporary border controls were first introduced. This paper aims to provide a deeper understanding of portrayals in the news media and everyday mobility experiences relating to border controls, with a special focus on Hyllie Station in the city of Malmö, in southern Sweden. The article is based on a compilation of the way Hyllie Station has been depicted in the news media for a deeper contextual understanding combined with a public participation GIS dataset focusing on the safety and security experiences of public transport nodes. Hyllie station, border controls and the presence of security personnel are depicted in different ways and at different levels. The complexities of life are intensified due to the daily cross-border commute and extra time required by border controls. The issue of mobility justice is raised, and the interrelationship between power and mobility becomes more and more tangible. Hyllie is a traffic node wherein the barriers of mobility and immobility become blurred, and power is displayed on different levels.

MEDIS—A comprehensive spatial database on Mediterranean islands for biogeographical and evolutionary research

AbstractMotivationThe intrinsic characteristics of islands make them a unique study system for the investigation of ecological and evolutionary dynamics. The Mediterranean Basin, an island‐rich biodiversity hotspot, still lacks a comprehensive spatial database for these geographic features. This study presents the first comprehensive spatial database of all Mediterranean islands larger than 0.01 km2, aiding ecological investigations and interdisciplinary research.Main types of variable containedThe MEDIS spatial database offers detailed information on 39 geographic, climatic, ecological and land‐use variables, including island area, perimeter, isolation metrics, climatic space, terrain data, land cover, palaeogeography, road networks and geological information, providing a multifaceted view of each island's characteristics.Spatial location and grainThe study encompasses 2217 islands in the Mediterranean Basin larger than 0.01 km2. The spatial grain of the datasets on which the selected variables are based varies from 10 m (ESA WorldCover) to 1 km (CHELSA‐BIOCLIM+).Time period and grainThe spatial database incorporates data from various sources, each with its own timeframe, such as the Global Shoreline Vector from 2014 Landsat imagery and the WorldCover dataset from 2021. Historical data like the Paleocoastlines GIS dataset offer insights into island configurations during the Last Glacial Maximum.Major taxa and level of measurementWhile not focusing on specific taxa, the study lays the foundation for comprehensive research on Mediterranean islands, facilitating comparisons and investigations into the distribution of native, endemic or alien species. The level of measurement is extensive, encompassing a wide range of variables and providing polygonal features rather than centroids' coordinates.

Analysis of urban metabolism in an informal settlement using the MuSIASEM method in Lima

By 2050, 68% of the global population will reside in cities, driving rapid urban growth and intensifying demand for scarce ecological resources within the Water-Food-Energy nexus. Social metabolism quantifies energy and material transformations with a social focus, building upon urban metabolism. Its application in resource-scarce informal settlements (ISs) has the potential to enhance their sustainability significantly. As community dynamics evolve, acknowledging society as a dynamic variable within this framework becomes increasingly relevant. Our study employs the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) framework, focusing on key variables: human activity, land use, money, energy, water, waste, and food. Based on surveys, interviews, GIS datasets, and statistical information, the study investigates the Ciudad de Gosen IS in Lima, Peru. The results show that, in the socio-economic dimension, 43% of the time employed is directed to the unpaid work sector. Notably, 71% of women and 29% of men spend a mean of 44 h/week/person caring for children or elderly. In the paid work sector, there are gender asymmetries; men have a salary 54% higher than women. In the ecological dimension, more than 78% of the homes have access to basic services, unlike other informal settlements in Latin America and Africa.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation phenology, climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

A CFD-BASED AIR QUALITY DISPERSION MODELING FOR URBAN AREAS USING OPENFOAM

Abstract. Air pollution in urban areas posed a significant threat to public health and environmental sustainability. This research addressed the urgent need for accurate air quality monitoring systems in urban environments, focusing on the University of the Philippines-Diliman as the study area. The study used 3D modeling and Computational Fluid Dynamics (CFD) to simulate and assess air quality dispersion in the urban setting of the Campus. The research objectives included: (a) developing a 3D model of the Campus using LiDAR and geospatial techniques, (b) modeling the three-dimensional PM2.5 dispersion in the area using CFD, and (c) validating the effectiveness of the CFD-based PM2.5 dispersion model using on-ground air quality monitoring data. The LiDAR and GIS datasets, including Digital Terrain Models, Digital Surface Models, orthophotos, building footprints, and road networks, were utilized to create the 3D campus model. Meteorological data (wind speed, direction, cloudiness, solar irradiation), and emission parameters (pollutant sources and concentrations) were integrated into the model for CFD simulations. SimFlow, an OpenFOAM-based software, facilitated the dispersion modeling process. The study's results revealed the impact of urban factors on PM2.5 dispersion, highlighting challenges in areas with restricted air movement due to buildings and narrow streets. Discrepancies between model predictions and on-ground measurements suggested the influence of unaccounted local factors. Nevertheless, the model demonstrated its utility in capturing general PM2.5 trends. In conclusion, the combination of 3D modeling and CFD simulation proved to be a robust approach for urban air quality monitoring. While improvements were needed to address local influences, this research provided a foundation for better air quality comprehension and urban management, essential for achieving sustainable cities and climate action goals.

Icings in the Selenga River basin

ABSTRACT In this study, we present a GIS dataset and map of icings formed in the transboundary Selenga River basin (largest tributary of the Lake Baikal). The GIS dataset of icings is compiled using Landsat and Sentinel-2 satellite images, as well as materials from the field expedition studies. It contains 29,054 individual icings with a total area of 1154.2 km2 (0.25% of the entire basin area), including at least 56 giant icings with an area ≥ 1 km2. Icings are widespread throughout the basin, but the highest icing coverage is found on the northern slopes of Hangayn ridge in the southeastern part of the basin, where most of the giant icings are located. The highest density of icings (more than 200 per km2) is found in the eastern part of the basin. Since icings can indicate climate-driven changes in streamflow and permafrost, the presented dataset will form the basis for future studies of these transformations.

Putting down roots: Relationships between urban forests and residents’ place attachment

Many cities seek to improve their urban forests to provide a diverse range of benefits for residents. One of these potential benefits, place attachment, is linked with its own positive outcomes including increased social cohesion, life satisfaction, and environmentally-friendly behaviour. However, it is currently unclear how urban forests relate to place attachment. This case study sought to clarify that relationship. A survey measuring residents’ place attachment, satisfaction/dissatisfaction with urban forest services/disservices respectively, perceived urban forest characteristics, and sociodemographic variables was distributed to residents of the Perth Metropolitan Area in Western Australia. The survey measured these variables at three spatial scales: residents’ homes, streets, and neighbourhoods. The 638 responses were combined with GIS datasets to calculate objective measures of average canopy cover. SEM was used to test relationships between different groups of variables and understand their relationship with place attachment. We found that place attachment was positively associated with residents’ satisfaction with key urban forest services: sense of place, social interactions encouraged by the trees, and fresh air. In turn, place attachment and satisfaction with services were associated with objective canopy cover, both directly and indirectly via perceived characteristics. An analysis of qualitative feedback from residents helped to illustrate nuances around how urban trees relate to sense of place and how the loss of trees can be a source of distress and solastalgia. As such, we recommend that actual and perceived urban forest characteristics, residents’ agency, and tree removal be adequately considered in efforts to maximise urban forest benefits.

Urban hydrological model (UHM) developed for an urban flash flood simulation and analysis of the flood intensity sensitivity to urbanization

An urban hydrological model (UHM) forced by radar-observed rainfalls was developed for producing rainstorm-related urban inundation maps for obtaining flash flood forecasts. The parameter sets of the land use and land cover (LULC) and urban drainage capacity derive from satellite multispectral images and high spatial resolution GIS datasets, relating to the urban hydrology and hydraulic properties. The hydrodynamics model was based on the simplified shallow water equation (SWE) neglecting the convective acceleration and pressure terms in the St. Venant equation. The intense convective rainstorm that induced heavy flash floods on 21 Jul. 2012 and the large-scale stratiform precipitation that occurred from 19 Jul. to 21 Jul. 2016 in Beijing were deliberately selected to perform flood-simulation and model-validation case studies. The simulation of severe flooding scenarios in the 2012 event was fairly reproduced and verified using media reports. In the case studies, the sensitivity tests verified that the flood intensity (1) would be enhanced slightly on pure impervious surfaces, (2) would be reduced significantly on pure pervious surfaces, and (3) would increase by 30%–60% without a drainage system. No false alarm could be perceived in the simulation of the 2016 event in a prolonging stratiform precipitation. The case studies confirmed the perspectives of using the hydrological model fed by QPEs/QPFs for flash flood forecasting and concluded that the hourly precipitation surpassing 30 mm/h would be indicative in impending flash floods in Beijing metropolis.

Comparing methods and data sources for classifying bicycle level of traffic stress: How well do their outcomes agree?

Level of Traffic Stress (LTS) metrics are widely used to examine how bicyclists may perceive stress along urban streets and identify opportunities for infrastructure improvements. The intuitiveness of the original method, which condensed 18 input variables into four levels, has made LTS very popular among practitioners. Nonetheless, it can be challenging to collect all required inputs. In response, numerous alternative methods have been developed with fewer or different inputs drawn from more general sources, such as OpenStreetMap (OSM) or GIS datasets from local agencies. These methods tend to use the same four-level schema, suggesting that they are commensurate, though this may be an inappropriate assumption. We examine agreement between seven LTS methods calculated from three data sources throughout two major U.S. cities and find substantial differences between many outcomes. Interestingly, the simplest LTS method and the least precise dataset, OSM, provided the most consistent outcomes. This suggests using simpler albeit less precise approaches to improve commensurability between LTS analyses. For more detailed analyses, we recommend site studies with on-the-ground measurements rather than relying on LTS to characterize subtleties. We also encourage clear labeling of LTS methods and data sources to avoid confusion about how results can be interpreted and compared.

Multi-Scalar Oblique Photogrammetry-Supported 3D webGIS Approach to Preventive Mining-Induced Deformation Analysis

Underground coal mining will inevitably cause serious ground deformation, and therefore, preventive mining-induced deformation analysis (MIDA) is of great importance in assisting mining planning and decision-making. Current web-based Geographic Information System (webGIS)-based applications usually use 2D GIS data and lack a holistic framework. This study presents a multi-scalar oblique photogrammetry-supported unified 3D webGIS framework for MIDA applications to fill this gap. The developed web platform uses multiple open-source JavaScript libraries, and the prototype system provides user-friendly interfaces for GIS data collecting and corresponding database establishment, geo-visualization and query, dynamic prediction, and spatial overlapping analysis within the same framework. The proposed framework was tested and evaluated in the Qianyingzi mining area in eastern China. The results demonstrated that multi-scalar oblique photogrammetry balances data quality and acquisition efficiency and provides a good source of GIS datasets, and the web-based platform has a good absolute and relative spatial accuracy verified by two types of validation data. Practical application results proved the feasibility and reliability of the system. The developed web-based MIDA prototype system attains an appealing performance and can be easily extended to similar geoscience applications.

Safety performance analysis of horizontal curves in urban areas

While numerous studies have examined horizontal curve risk factors in rural areas, there is only one study for urban areas. Moreover, previous studies have used limited datasets, which tend to generate an intrinsic bias on results either by the sample size or due to a lack of understanding of all the risk factors associated with curve safety.This study aims to narrow this knowledge gap in three aspects: it focuses on urban areas; it uses a large novel GIS dataset of about 25,000 urban curves; and it expands the traditional curve risk factor pool by examining the spatial relationship of curves to adjacent curves and intersections. Using this curve dataset and six years of statewide fatal and injury crash data in the state of Florida, the study develops customized safety performance functions (SPFs) for urban curves based on different spatial relationships of curves to intersections.The results confirm that the traditional risk factors for rural curves, such as traffic volume, curve radius and length, speed limit, functional classification, and the number of lanes, also apply to curves in urban areas. However, the new finding is that curve safety in urban areas is affected by the proximity of curves to adjacent curves and intersections. The curves with intersections and isolated curves (with no adjacent nearby curves) are at high risk. There are also risk factor differences between single and dual-centerline roads. We also observed differences between the travel directions on divided roadway curves, but these differences will require more research.

Historical Atlas of the Low Countries: A gis Dataset of Locality-Level Boundaries (1350–1800)

Abstract This historical gis provides reconstructions of premodern village-level boundaries in the Low Countries, covering the present-day Netherlands, Belgium, Luxembourg and adjacent parts of France and Germany. While some countries have historical municipal gis maps for the nineteenth and twentieth centuries, this resource provides a detailed and contiguous historical gis for the premodern period. It also covers an area spanning several modern states, a rare undertaking. It combines modern and historical sources, including existing gis data, historical maps, and written records, focusing on the ‘smallest mappable units’ referred to as administrative areas in contemporary documents, such as parishes, lordships, or towns. This article accompanies the first tranche of the dataset for the 1500 cross-section, which can be used to link statistics for any period between 1350 and 1800. When completed, the gis dataset will cover four cross-sections: 1350, 1500, 1650, and 1800. Each cross-section will comprise an estimated 18.000 geographical units – typically around 5 to 10 km2 each. The dataset facilitates cross-temporal analysis of historical statistics while allowing users to consider the wider geographical and territorial micro and macro context of these statistics. The result will be an invaluable resource for historians, archaeologists, geographers, and social scientists alike.

Ecosystems conservation and analysis of the representativeness of protected areas of the Kyrgyz Republic in a climate change

The most valuable natural complexes and objects should be represented in the State protected areas (PA) system. In order to preserve biodiversity, it is necessary to expand protected lands to 10% of the total country area and create an ecological network that includes various categories of ecosystems. Each protected area can represent one or a number of ecosystems, depending on the height above sea level, landform, climate and other factors. In a perfect scenario, PA system of Kyrgyz Republic must represent all categories of ecosystems including mountain, steppe, semi-desert and desert. In this research the analysis of the representativeness of protected areas in Kyrgyz Republic was performed for the first time. For this purpose, GIS data sets for PAs and classification of ecosystems were used. According to the results, forest ecosystems have the highest coverage, whereas low mountain meadows and wetlands have the lowest coverage in PA system of Kyrgyz Republic. This research suggests that this type of analysis is an important tool for biodiversity conservation and evaluation of an ecological vulnerability.

Simulation and Prediction Algorithm for the Whole Process of Debris Flow Based on Multiple Data Integration

In order to solve the problems of large errors and low accuracy in debris-flow forecasting, the simulation and prediction algorithm for the whole process of debris flow based on multiple data integrations is studied. The middleware method is used to integrate multiple GIS data sets, and the GIS spatial database after multiple data integrations is used to provide the basis of data for the whole process simulation and prediction of debris flow. The spatial cellular simulation model of debris flow is built using the cellular automatic mechanism. The improved kernel principal component analysis method is used to reduce the dimension of debris-flow prediction index data. The reduced dimension index data is input into the support vector machine, and the support vector machine is used to output the prediction results of debris flow in the space cell simulation model of debris flow. Through the simulation visualization technology, the dynamic display of the simulation prediction of the whole process of debris flow is carried out. The experimental results show that the algorithm can realize the simulation of the whole process of debris-flow changes, that the prediction results of debris flow are close to the actual results, and that the error is less than 5%, which improves the prediction accuracy of debris flow and can be used as the auxiliary basis for relevant decision-making departments.

Using urban building energy modeling to quantify the energy performance of residential buildings under climate change.

The building sector is facing a challenge in achieving carbon neutrality due to climate change and urbanization. Urban building energy modeling (UBEM) is an effective method to understand the energy use of building stocks at an urban scale and evaluate retrofit scenarios against future weather variations, supporting the implementation of carbon emission reduction policies. Currently, most studies focus on the energy performance of archetype buildings under climate change, which is hard to obtain refined results for individual buildings when scaling up to an urban area. Therefore, this study integrates future weather data with an UBEM approach to assess the impacts of climate change on the energy performance of urban areas, by taking two urban neighborhoods comprising 483 buildings in Geneva, Switzerland as case studies. In this regard, GIS datasets and Swiss building norms were collected to develop an archetype library. The building heating energy consumption was calculated by the UBEM tool-AutoBPS, which was then calibrated against annual metered data. A rapid UBEM calibration method was applied to achieve a percentage error of 2.7%. The calibrated models were then used to assess the impacts of climate change using four future weather datasets out of Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results showed a decrease of 22%-31% and 21%-29% for heating energy consumption, an increase of 113%-173% and 95%-144% for cooling energy consumption in the two neighborhoods by 2050. The average annual heating intensity dropped from 81 kWh/m2 in the current typical climate to 57 kWh/m2 in the SSP5-8.5, while the cooling intensity rose from 12 kWh/m2 to 32 kWh/m2. The overall envelope system upgrade reduced the average heating and cooling energy consumption by 41.7% and 18.6%, respectively, in the SSP scenarios. The spatial and temporal distribution of energy consumption change can provide valuable information for future urban energy planning against climate change.