Consistent Dataset Research Articles

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.

Extended relativistic multiconfiguration calculations of energy levels and transition properties in singly ionized tin

Multiconfiguration Dirac-Hartree-Fock (MCDHF) and relativistic configuration interaction (RCI) calculations are performed for 22 states in singly ionized tin (Sn II) belonging to the 5s2ns(n=6,7), 5s2nd(n=5,6), 5s5p2 even parity configurations and the 5s2np(n=5,6,7), 5s24f odd parity configurations. Valence-valence and core-valence correlation effects are taken into account through configuration state function (CSF) expansions. Complete and consistent data sets of level energies, wavelengths, oscillator strengths, lifetimes and transition rates among all these states are given. The results are compared with existing theoretical and experimental results. There is an excellent agreement for calculated excitation energies with experimental data from the NIST database. Lifetimes and transition rates are also in agreement with the results from previous calculations and available measurements for most of the transitions.

Crucial feature space for ductile bcc high-entropy alloys

Body-centered cubic (bcc) high-entropy alloys (HEAs) are promising structural materials for nuclear power plants to ensure good radiation resistance. However, the majority of bcc HEAs show limited room temperature ductility even in compression. In addition to the compressive properties of as-cast high-activation bcc HEAs collected from the literature, those of low-activation ones were investigated by phase diagram calculations and experiments. Therefore, a consistent dataset comprising 93 samples was generated. A classification and regression tree (CART) algorithm was employed to differentiate the target bcc HEAs with a compressive failure strain of more than 50% from the others. The model generality of a finalized CART classifier was validated by training and testing F1 scores and accuracies. It was found that Pugh's ratio (κ) and valence electron concentration (VEC) are two key attributes to identify the target alloys. The crucial κ-VEC feature space displays that the targets are generally located in the region where κ is larger than 3.129 or VEC is larger than 6.296. Especially, high-activation and low-activation samples seem to have opposite characteristics, motivating the further study of a deformation mechanism for low-activation bcc HEAs.

Towards a continent-wide ecological site condition database using calibrated expert evaluations.

A cost-effective way of undertaking comprehensive, continental-scale, assessments of ecological condition is needed to support large-scale conservation planning, monitoring, reporting, and decision-making. Currently, cross-jurisdictional inconsistency in assessment methods limits the capacity to scale-up monitoring. Here we present a novel way to build a coherent continent-wide site-level ecological condition dataset, using cross-calibration methods to integrate assessments from many observers. We focus on the use of condition assessments from individual expert observers, a currently untapped resource. Our approach has two components: (1) a simple online tool that captures expert assessments at specific locations; (2) a process of calibrating and rescaling disparate expert evaluations that can be applied to the data to provide a consistent dataset for use in conservation assessments. We describe a pilot study, involving 28 experts, who contributed 314 individual site condition assessments across a wide range of ecosystems and regions throughout continental Australia. A correction factor for each expert was used to rescale the contributed site condition assessment scores, based on a set of 77 photographic images, each scored for their condition by multiple experts, using a linear mixed model. Our approach shows strong promise for delivering the volumes of data required to develop continental-scale reference libraries of site condition assessments. Although developed from expert elicitation, the approach could also be used to harmonize the collation of existing condition datasets. The process we demonstrate can also facilitate online citizen scientists to make site condition assessments that can be cross-calibrated using contributed images.

ENECO2Calc—A Modeling Tool for the Investigation of Energy Transition Paths toward Climate Neutrality within Municipalities

The paper focuses on developing an energy-modeling tool called ENECO2Calc, which allows the determination of current ecologic and economic footprints based on calculating the final energy demand within several sectors for municipalities. Furthermore, different energy transition paths until 2050 can be investigated and compared to the business-as-usual reference scenario. ENECO2Calc is the first municipality-based energy-modeling tool that allows the development of meaningful scenarios until 2050 by considering climate policy goals and RES potentials, and it involves the mobility emission forecast tool “PROVEM”. ENECO2Calc is exclusively based on consistent statistical datasets. Additionally, the energy-modeling process was demonstrated as exemplary for the Austrian municipality St. Margareten im Rosental. For the selected municipality, three different scenarios were investigated. It could be concluded that a mix of decentral RES technologies and central cogeneration units in the heat sector, a mix of solar PV and cogeneration units in the electricity sector, and the use of synthetic biofuels coupled with a higher share of electrification in the fuel sector seemed to be most promising in the considered region. ENECO2Calc is a helpful energy-modeling tool toward climate neutrality to support municipalities in developing appropriate economic and ecological footprint strategies.

Consistency analysis of forest height retrievals between GEDI and ICESat-2

Two space-borne LiDAR missions (Global Ecosystem Dynamics Investigation, GEDI; Ice, Cloud, and land Elevation Satellite-2, ICESat-2) have unique advantages in retrieving forest heights. Fusing these two missions will greatly increase the number of forest height samples and realize their geographical complementarity, providing unprecedented opportunities for global forest height mapping. However, ICESat-2 and GEDI use different LiDAR technologies, which may lead to inconsistencies in the forest height estimation between the two missions. This study aims to explore the potential of obtaining consistent forest heights from ICESat-2 and GEDI data in support of global forest height mapping. First, the accuracies of GEDI and ICESat-2 forest heights in four different scenarios (nigh/daytime and strong/weak beam) were validated and compared utilizing airborne LiDAR data. Second, we quantitatively evaluated the differences in the forest heights derived from the GEDI and ICESat-2 data within their overlapping footprints and analyzed the effects of the terrain slope, forest coverage, forest type and study site on these differences. Third, the forest height consistency models were built based on GEDI-derived forest heights and ICESat-2 feature parameters using stepwise regression (SR) and random forest (RF) algorithms as well as a combination of both SR and RF algorithms. Finally, we evaluated the transferability of forest height consistency models to different forest types and study sites, and further analyzed the potential of building a universally consistent model. The results showed that the accuracies of both GEDI and ICESat-2 derived forest heights differ among four different scenarios, and the accuracy of forest heights extracted from GEDI data (R2 = 0.93, RMSE = 2.99 m for power beams at night) is higher than that extracted from ICESat-2 data (R2 = 0.78, RMSE = 4.62 m for strong beams at night) regardless of scenarios. The forest height differences exist between these two missions, and show an apparent change trend with increasing forest coverage. By establishing the consistency models, the forest height difference between GEDI and ICESat-2 can be reduced. Compared to the forest height consistency models built by SR or RF algorithms, the consistency models established by combining the RF and SR algorithms have higher accuracy (average R2 = 0.86, RMSE = 2.56 m for ATL08). Additionally, the consistency models developed specifically for one forest type or study site are less transferable, while a universal consistency model is applicable for various vegetation types and study sites. After building a universal consistency model based on both SR and RF algorithms, the consistent forest heights were obtained from GEDI and ICESat-2 data. Overall, this study demonstrates the possibility of combining different modes of space-borne LiDAR data to obtain consistent forest height datasets.

Structure-property relationships in carbon electrochemistry

Carbonaceous nanomaterials can be a game changing materials in many technological fields, especially in electroanalytical applications. However, there is no consensus on the associations between the structure and electrochemical performance of these nanomaterials – even for the most basic electrochemical properties. This challenge stems from the fact that typically carbonaceous nanomaterials are obtained from various sources and not characterized properly. Therefore, to solve this deadlock we carry out systematic electrochemical characterization for a set of in-house fabricated as well as physicochemically thoroughly characterized carbon nanomaterials. We will then proceed to establish structure – performance associations for these materials. In addition, we will highlight how sensitive the electrochemical performance of these materials can be to small changes in their structural properties. Further, we emphasize the lack of correlation between electrochemical performance of electrode materials as determined using outer sphere redox (OSR) and inner sphere redox (ISR) probes the latter being highly analyte specific. As a first consistent set of electrochemical data obtained by using well characterized carbonaceous nanomaterials, this work will provide solid basis to expand the use of these materials in more complex electroanalytical as well as other applications.

Global inventory of suitable, cultivable and available cropland under different scenarios and policies

Where land-use change and particularly the expansion of cropland could potentially take place in the future is a central research question to investigate emerging trade-offs between food security, climate protection and biodiversity conservation. We provide consistent global datasets of land potentially suitable, cultivable and available for agricultural use for historic and future time periods from 1980 until 2100 under RCP2.6 and RCP8.5, available at 30 arc-seconds spatial resolution and aggregated at country level. Based on the agricultural suitability of land for 23 globally important food, feed, fiber and bioenergy crops, and high resolution land cover data, our dataset indicates where cultivation is possible and how much land could potentially be used as cropland when biophysical constraints and different assumptions on land-use regulations are taken into account. By serving as an input for land-use models, the produced data could improve the comparability of the models and their output, and thereby contribute to a better understanding of potential land-use trade-offs.

Prevalence of Autotrophy in Non-humic African Lakes

Heterotrophic respiration of organic matter (OM) is thought to dominate over aquatic primary production (PP) in most freshwater lake ecosystems. This paradigm implies that lateral transport of OM from the terrestrial biosphere subsidize the major fraction of aquatic respiration and that many lakes are a net source of carbon dioxide (CO2) to atmosphere. Nevertheless, African lakes were absent of the datasets upon which this paradigm was built. Here, we report a comprehensive and methodologically consistent data set of pelagic PP and community respiration (CR) obtained over the last decade in contrasting non-humic African lakes including 5 of the East African Great lakes (Tanganyika, Kivu, Edward, Albert, Victoria) and smaller shallow lakes located in Eastern Africa. Also, we determined the partial pressure of CO2 in surface waters and examined the sources and dynamics of organic and inorganic carbon by means of stable isotope tools across a wide range of physical and chemical conditions and productivity status. Our observations revealed that the threshold value at which the equivalence between PP and CR is met is substantially lower in Africa (10 mmol C m−3 d−1) than at higher latitude (25 mmol C m−3 d−1), suggesting that non-humic African lakes tend to be more autotrophic than expected from empirical relationships derived from data collected in boreal and temperate regions. Integrated at the regional scale, we estimate that PP is about 20 times higher than the organic carbon burial in sediments. It implies that a large fraction (< 90%) of PP is effectively recycled in the warm water column of non-humic African lakes.

Accurate phenology analyses require bud traits and energy budgets.

Spring phenology is mainly driven by temperature in extratropical ecosystems. Recent evidence highlighted the key role of micrometeorology and bud temperature on delaying or advancing leaf unfolding. Yet, phenology studies, either using ground-based or remote sensing observations, always substitute plant tissue temperature by air temperature. In fact, temperatures differ substantially between plant tissues and the air because plants absorb and lose energy. Here, we build on recent observations and well-established energy balance theories to discuss how solar radiation, wind and bud traits might affect our interpretation of spring phenology sensitivity to warming. We show that air temperature might be an imprecise and biased predictor of bud temperature. Better characterizing the plants' phenological response to warming will require new observations of bud traits and temperature for accurately quantifying their energy budget. As consistent micrometeorology datasets are still scarce, new approaches coupling energy budget modelling and plant traits could help to improve phenology analyses across scales.

In situ estimates of net primary production in the open‐ocean Gulf of Mexico

AbstractNet primary production (NPP) measurements in the open‐ocean Gulf of Mexico are scarce and mostly limited to satellite‐derived surface estimates. Profiling floats can obtain depth‐resolved chlorophyll (Chl) and backscattering (bbp) data for NPP estimates, but historical float data in this region lacked field calibrations. We adjusted the float data with satellite Chl, generating a consistent dataset for deriving the first year‐round depth‐resolved NPP estimates in this region. The results revealed distinct seasonal patterns in surface NPP and depth‐integrated (0–100 m) total NPP, with the peak values in the winter and summer, respectively. The results showed that for most of the year subsurface (20–100 m) contributes more to the total (0–100 m) NPP than the surface layer (0–20 m), and the major controls of NPP in these two layers were light level and nutrient supply, respectively.

Comparison and evaluation of multiple satellite aerosol products over China in different scenarios under a unified criterion: Preparation for consistent and high-quality dataset construction

Aerosol optical depth (AOD) products provided by satellite sensors are widely used in climate modeling and climate change research. However, inconsistencies between products can cause large biases. It is necessary to integrate multiple satellite aerosol products to produce a consistent and high-quality aerosol record that satisfies the requirements of climate research users. To effectively combine the advantages of different datasets, their performance under different conditions needs to be compared during overlapping periods using uniform methods and criteria. Based on Aerosol Robotic Network (AERONET) measurements from 2019 to 2021, this study is the first to systematically compare the accuracy of 16 publicly released products from multiple satellite sensors in China under various scenarios using the same criteria to support the construction of a harmonized and high-quality dataset. Compared with AERONET AOD, the average retrieval percentage of 16 products satisfying the Global Climate Observing System (GCOS) accuracy requirement is 26.51%, with MISR and VIIRS Deep Blue (DB) products exceeding 40%, while MODIS 3 km Dark Target (DT), VIIRS DT, VIIRS Enterprise Processing System (EPS), and Sentinel-3 products are below 20%. By quantifying the accuracy of the products under different surface conditions, aerosol loadings, and particle size types, the accuracy ranking matrix of 16 products under different scenarios is established to provide a priori knowledge for the construction of a merged dataset. Finally, AOD products from October 2020 are merged based on the accuracy ranking matrix. The merged results show that, compared to the commonly used MODIS DT product, the percentage of retrievals satisfying GCOS requirement is improved by 22.55%, the correlation coefficient and mean absolute error are also improved, and the national average AOD coverage increases from 22.18% to 47.73%. These results are expected to provide guidance for the construction of high-quality datasets and the application of satellite aerosol products in climate research.

Soil organic carbon (SOC) prediction in Australian sugarcane fields using Vis–NIR spectroscopy with different model setting approaches

To maintain profitability in sugarcane areas of Australia, soil nutrients need to be applied to replace losses to biomass production. For example, nitrogen fertiliser requires consideration of soil organic carbon (SOC, %). However, determining SOC is time-consuming. An alternative is to use a visible–near infrared (Vis–NIR) spectroscopy library. Herein, a Vis–NIR library is developed to predict topsoil (0–0.3 m) SOC using partial least squares regression (PLSR) and machine learning (i.e., Cubist, random forest [RF] and support vector machine [SVM]) in four sugarcane districts (i.e., Mossman, Lannercost, Herbert, and Proserpine). Different approaches were compared (i.e., site-specific, site-independent, hold-out and spiking) with spike size also considered. In all comparisons, a consistent set of calibration and validation data were used. The calibration coefficient of determination (R2) was always strong (> 0.7), and generally better than the validation R2, regardless of the modelling approach, district, or spike size. For the validation, the Lin's concordance correlation coefficient (LCCC) showed PLSR (0.92, and 0.9) and Cubist (0.91 and 0.9) were close to perfect (> 0.9) for site-specific and site-independent, respectively. This was not the case for hold-out, with only strong R2 (0.71) and substantial agreement (0.80) in Herbert using Cubist and moderate overall using PLSR. Similar results were achieved in terms of the accuracy considering the ratio of performance to interquartile (RPIQ), whereby overall site-specific and site-independent approaches had excellent accuracy (> 2.5) with Cubist slightly more accurate than PLSR. Hold-out accuracy was generally very poor (< 1.4). Spiking the hold-out data sets produced mixed results with prediction R2, agreement and accuracy respectively best in Lannercost with 70 or more samples using PLSR (strong, substantial and excellent) and Hebert with 10 or more using Cubist (strong, near perfect and excellent), while in Mossman with 50 or more samples using SVM (very weak, poor and fair) and Proserpine with 30 or more samples using Cubist (weak, moderate and fair) the results were not as good. It can be concluded that either site-specific or site-independent approach to calibration and prediction using either PLSR or Cubist was best, with the use of the latter approach being more efficient and allowing for the potential to add to this spectral library when new samples from each area or new areas can be added.

Challenges in Understanding the Variability of the Cryosphere in the Himalaya and Its Impact on Regional Water Resources

The Himalaya plays a vital role in regulating the freshwater availability for nearly a billion people living in the Indus, Ganga, and Brahmaputra River basins. Due to climate change and constantly evolving human-hydrosphere interactions, including land use/cover changes, groundwater extraction, reservoir or dam construction, water availability has undergone significant change, and is expected to change further in the future. Therefore, understanding the spatiotemporal evolution of the hydrological cycle over the Himalaya and its river basins has been one of the most critical exercises toward ensuring regional water security. However, due to the lack of extensivein-situmeasurements, complex hydro-climatic environment, and limited collaborative efforts, large gaps in our understanding exist. Moreover, there are several significant issues with available studies, such as lack of consistent hydro-meteorological datasets, very few attempts at integrating different data types, limited spatiotemporal sampling of hydro-meteorological measurements, lack of open access toin-situdatasets, poorly accounted anthropogenic climate feedbacks, and limited understanding of the hydro-meteorological drivers over the region. These factors result in large uncertainties in our estimates of current and future water availability over the Himalaya, which constraints the development of sustainable water management strategies for its river catchments hampering our preparedness for the current and future changes in hydro-climate. To address these issues, a partnership development workshop entitled “Water sEcurity assessment in rIvers oriGinating from Himalaya (WEIGH),” was conducted between the 07th and 11th September 2020. Based on the intense discussions and deliberations among the participants, the most important and urgent research questions were identified. This white paper synthesizes the current understanding, highlights, and the most significant research gaps and research priorities for studying water availability in the Himalaya.

An integrated dataset of daily lake surface water temperature over the Tibetan Plateau

Abstract. Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an evident indicator of climate change. By combining the strengths of satellite-based observation and modeling, we have produced an integrated daily LSWT for 160 lakes across the Tibetan Plateau where in situ observation is limited. The MODIS-based lake-wide mean LSWT in the integrated dataset includes the daytime, nighttime, and daily mean for the period 2000–2017. The MODIS-based daily mean LSWT is used to calibrate a simplified physically based model (i.e., modified air2water model), upon which a complete and consistent daily LSWT dataset is reconstructed for the period 1978–2017. The reconstructed LSWT dataset is validated by comparing it with both the satellite-based and in situ observations. The validation shows that the reconstructed LSWT is in good agreement with the observations. According to the reconstructed LSWT dataset, the annual LSWT of lakes in the Tibetan Plateau has increased significantly in the period 1978–2017 with an increase rate ranging from 0.01 to 0.47 ∘C per 10 years. The warming rate is higher in winter than in summer. The integrated dataset is unique for its relatively large temporospatial span (1978–2017) and high temporal resolution. The dataset together with the methods developed can contribute to research in exploring water and heat balance changes and the consequent ecological effects at the Tibetan Plateau. Data from this study are openly available via the Zenodo portal, with DOI https://doi.org/10.5281/zenodo.6637526 (Guo et al., 2022).

Mangroves Cover Change Trajectories 1984-2020: The Gradual Decrease of Mangroves in Colombia

Awareness of the significant benefits of mangroves to human lives and their role in regulating environmental processes has increased during the recent decades. Yet there remains significant uncertainty about the mangrove change trajectories and the drivers of change at national scales. In Colombia, the absence of historical satellite imagery and persistent cloud cover have impeded the accurate mapping of mangrove extent and change over time. We create a temporally consistent Landsat-derived dataset using the LandTrendr algorithm to track the historical land cover and mangrove conversion from 1984-2020 across Colombia. Over this period, mangrove extent decreased by ~48.000ha (14% of total mangrove area). We find a gradual reduction of mangrove extent along the Pacific coast since 2004, whereas, in the Caribbean, mangrove cover declined around during 1984-1988 and also after 2012. Our time-series analysis matches with drivers of mangrove change at three local sites. For instance, hydroclimatic events, dredging activities, and high sediment loads transported by the rivers have collectively improved mangrove recovery in some sites. In contrast, human activities pressure linked to agricultural expansion and road construction have degraded mangroves. The transition from dense mangrove to other vegetation types is the most significant conversion affecting mangrove cover in Colombia, impacting an area of 38,469 ± 2,829 ha. We anticipate increased mangrove loss, especially along the Pacific coast, resulting from intensified human activity. Prioritization of conservation areas is needed to support local institutions, maintain currently protected areas, and develop strategies (e.g. payment for ecosystem services) to preserve one of the most pristine mangrove regions in the Western Hemisphere.

A consistent and corrected nighttime light dataset (CCNL 1992–2013) from DMSP-OLS data

Remote sensing of nighttime light can observe the artificial lights at night on the planet’s surface. The Defense Meteorological Satellite Program’s Operational Line Scan (DMSP-OLS) data (1992–2013) provide planet-scale nighttime light data over a long-time span and have been widely used in areas such as urbanization monitoring, socio-economic parameters estimation, and disaster assessment. However, due to the lack of an on-board calibration system, sensor design defects, limited light detection range, and inadequate quantization levels, the applications of DMSP-OLS data are greatly limited by interannual inconsistency, saturation, and blooming problems. To address these issues, we used the power function model based on pseudo-invariant feature, the saturation correction method based on regression model and radiance-calibrated data (SARMRC), and the self-adjusting model (SEAM) to improve the quality of DMSP data, and generated a Consistent and Corrected Nighttime Light dataset (CCNL 1992–2013). CCNL dataset shows good performance in interannual consistency, spatial details of urban centers, and light blooming, which is helpful to fully explore the application potentials of long time series nighttime light data.

An International Library for Land Cover Legends: The Land Cover Legend Registry

Information on land cover is vital to numerous United Nations (UN) missions, including achieving the Sustainable Development Goals (SDGs). Because land cover data are developed by a variety of organizations for a range of objectives, they are based on different classification schemes and have discrepancies. In addition, the sustainability for land cover is hampered by limited access to information and documentation. Accordingly, international standards for land cover are developed to improve interoperability between different land cover datasets. However, the use and development of land cover datasets are limited by various factors including availability of properly documented land cover legends in support of different applications including change assessment, comparison, and international reporting. The purpose of this article is to highlight the importance of land cover in achieving several goals and to introduce the first international platform for land cover legend, named Land Cover Legend Registry (LCLR). This registry is a contribution to the international land cover community and the UN in effort to promote and support data harmonization processes and interoperability from local to global level, and vice versa. Users can not only use the registry for preparing consistent datasets, but also contribute to it by providing the latest data to ensure the long-term availability of both updated and existing datasets around the world. Moreover, building on the experience developing land cover legends with different nations, a brief explanation on the preparation of legends is also provided. Additionally, it is more important than ever to develop land cover registers to support the use, expansion, integration, and use uptake of land cover data, particularly for innovative remote sensing, machine learning, and information and communication technologies and techniques that build on existing and national contexts.

A Transformation in City-Descriptive Input Data for Urban Climate Models

In urban climate studies, datasets used to describe urban characteristics have traditionally taken a class-based approach, whereby urban areas are classified into a limited number of typologies with a resulting loss of fidelity. New datasets are becoming increasingly available that describe the three-dimensional structure of cities at sub-metre micro-scale resolutions, resolving individual buildings and trees across entire continents. These datasets can be used to accurately determine local characteristics without relying on classes, but their direct use in numerical weather and climate modelling has been limited by their availability, and because they require processing to conform to the required inputs of climate models. Here, we process building-resolving datasets across large geographical extents to derive city-descriptive parameters suitable as common model inputs at resolutions more appropriate for local or meso-scale modelling. These parameter values are then compared with the ranges obtained through the class-based Local Climate Zone framework. Results are presented for two case studies, Sydney and Melbourne, Australia, as open access data tables for integration into urban climate models, as well as codes for processing high-resolution and three-dimensional urban datasets. We also provide an open access 300 m resolution building morphology and surface cover dataset for the Sydney metropolitan region (approximately 5,000 square kilometres). The use of building resolving data to derive model inputs at the grid scale better captures the distinct heterogenetic characteristics of urban form and fabric compared with class-based approaches, leading to a more accurate representation of cities in climate models. As consistent building-resolving datasets become available over larger geographical extents, we expect bottom-up approaches to replace top-down class-based frameworks.

Toward Benchmarking of Long-Term Spatio-Temporal Maps of Pedestrian Flows for Human-Aware Navigation.

Despite the advances in mobile robotics, the introduction of autonomous robots in human-populated environments is rather slow. One of the fundamental reasons is the acceptance of robots by people directly affected by a robot’s presence. Understanding human behavior and dynamics is essential for planning when and how robots should traverse busy environments without disrupting people’s natural motion and causing irritation. Research has exploited various techniques to build spatio-temporal representations of people’s presence and flows and compared their applicability to plan optimal paths in the future. Many comparisons of how dynamic map-building techniques show how one method compares on a dataset versus another, but without consistent datasets and high-quality comparison metrics, it is difficult to assess how these various methods compare as a whole and in specific tasks. This article proposes a methodology for creating high-quality criteria with interpretable results for comparing long-term spatio-temporal representations for human-aware path planning and human-aware navigation scheduling. Two criteria derived from the methodology are then applied to compare the representations built by the techniques found in the literature. The approaches are compared on a real-world, long-term dataset, and the conception is validated in a field experiment on a robotic platform deployed in a human-populated environment. Our results indicate that continuous spatio-temporal methods independently modeling spatial and temporal phenomena outperformed other modeling approaches. Our results provide a baseline for future work to compare a wide range of methods employed for long-term navigation and provide researchers with an understanding of how these various methods compare in various scenarios.