Big data in support of the sustainable development goals(continued):a celebration of the establishment of the International Research Center of Big Data for Sustainable Development Goals(CBAS)

The rapid development of analytics and concepts in big data enables us to diversify our efforts and enhance opportunities to implement the Sustainable Development Goals(SDGs).Big data improves the extent to which scientific evidence and innova-tive technological solutions can be adopted to meet these goals.However,the tools and methods of big data are still somewhat of a novelty in this respect.

Big earth data for achieving the sustainable development goals in the belt and road region

The Belt and Road(B&R)is an important region with historical,economic,cultural,and political significance,including 75%of the global population and numerous social activ-ities.However,many countries along the B&R region are experiencing developmental challenges such as rapid urbanization,land degradation,water shortages,water and food security,frequent disasters,and large-scale ecosystem changes.The UN’s 17 Sustainable Development Goals(SDGs)provide a universal call to action to end poverty,protect the planet,and ensure that all people enjoy peace and prosperity by 2030 and to achieve economic,social,and environmental sustainability at global,regional,and national scales.

Innovative approaches to the Sustainable Development Goals using Big Earth Data

A persistent challenge for the Sustainable Development Goals(SDGs)has been a lack of data for indicators to assess progress towards each goal and varying capacities among nations to con-duct these assessments.Rapid developments in big data,however,are facilitating a global approach to the SDGs.Tools and data products are emerging that can be extended to and leveraged by nations that do not yet have the capacity to measure SDG indica-tors.Big Earth Data,a special class of big data,integrates multisource data within a geographic context,utilizing the principles and methodologies of the established literature on big data science,applied specifically to Earth system science.This paper discusses the research challenges related to Big Earth Data and the concerted efforts and investments required to make and mea-sure progress towards the SDGs.As an example,the Big Earth Data Science Engineering Program(CASEarth)of the Chinese Academy of Sciences is presented along with other case studies on Big Earth Data in support of the SDGs.Lastly,the paper proposes future priorities for developments in Big Earth Data,such as human resource capacity,digital infrastructure,interoperability,and envir-onmental considerations.

New Record Ocean Temperatures and Related Climate Indicators in 2023

The global physical and biogeochemical environment has been substantially altered in response to increased atmospheric greenhouse gases from human activities.In 2023,the sea surface temperature(SST)and upper 2000 m ocean heat content(OHC)reached record highs.The 0–2000 m OHC in 2023 exceeded that of 2022 by 15±10 ZJ(1 Zetta Joules=1021 Joules)(updated IAP/CAS data);9±5 ZJ(NCEI/NOAA data).The Tropical Atlantic Ocean,the Mediterranean Sea,and southern oceans recorded their highest OHC observed since the 1950s.Associated with the onset of a strong El Niño,the global SST reached its record high in 2023 with an annual mean of~0.23℃ higher than 2022 and an astounding>0.3℃ above 2022 values for the second half of 2023.The density stratification and spatial temperature inhomogeneity indexes reached their highest values in 2023.

地理大数据揭示中国主要城市建成环境物质存量的空间格局

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important,yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources,waste,and climate strategies.However,our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited,largely owing to the lack of sufficient high spatial resolution data.This study leveraged multi-source big geodata,machine learning,and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels.The per capita built environment stock of many cities(261 tonnes per capita on average)is close to that in western cities,despite considerable disparities across cities owing to their varying socioeconomic,geomorphology,and urban form characteristics.This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades.China’s urban expansion tends to be more“vertical”(with high-rise buildings)than“horizontal”(with expanded road networks).It trades skylines for space,and reflects a concentration-dispersion-concentration pathway for spatialized built environment stocks development within cities in China.These results shed light on future urbanization in developing cities,inform spatial planning,and support circular and low-carbon transitions in cities.

Big Earth data for disaster risk reduction

In an ever-changing world,where the frequency and intensity of natural and humanmade disasters are on the rise,disaster risk reduction has emerged as a crucial focal point of interdisciplinary research,governance,and public discourse.Disaster risk reduction,which aims to safeguard humans and protect environments from hazards and threats,is of high societal relevance and closely related to several of the United Nations Sustainable Development Goals(SDGs).The findings from research into disaster risk reduction contribute significantly to making cities and other settlements more inclusive,safe,resilient,and sustainable.

Classification framework and semantic labeling for Big Earth Data

Big Earth Data refers to the multidimensional integration and association of scientific data,including geography,resources,environment,ecology,and biology.An effective data classification system and label management strategy are important foundations for long-term management of data resources.The objective of this study was to construct a classification system and realize multidimensional semantic data label management for the Big Earth Data Science Engineering Program(CASEarth).This study constructed two sets of classification and coding systems that realize classification by mapping each other;namely,the geosphere-level and Sustainable Development Goals(SDGs)indicator classifications.This technique was based on natural language processing technology and solved problems with subject-word segmentation,weight calculation,and dynamic matching.A prototype system for classification and label management was constructed based on existing CASEarth datasets of more than 1,100.Furthermore,we expect our study to provide the methodology and technical support for useroriented classification and label management services for Big Earth Data.

Progress toward the sustainable development of world cultural heritage sites facing land-cover changes

The quantification of the extent and dynamics of land-use changes is a key metric employed to assess the progress toward several Sustainable Development Goals(SDGs)that form part of the United Nations 2030 Sustainable Development Agenda.In terms of anthropogenic factors threatening the conservation of heritage properties,such a metric aids in the assessment of achievements toward heritage sustainability solving the problem of insufficient data availability.Therefore,in this study,589 cultural World Heritage List(WHL)properties from 115 countries were analyzed,encompassing globally distributed and statistically significant samples of“monuments and groups of buildings”(73.2%),“sites”(19.3%),and“cultural landscapes”(7.5%).Land-cover changes in the WHL properties between 2015 and 2020 were automatically extracted from big data collections of high-resolution satellite imagery accessed via Google Earth Engine using intelligent remote sensing classification.Sustainability indexes(SIs)were estimated for the protection zones of each property,and the results were employed,for the first time,to assess the progress of each country toward SDG Target 11.4.Despite the apparent advances in SIs(10.4%),most countries either exhibited steady(20.0%)or declining(69.6%)SIs due to limited cultural investigations and enhanced negative anthropogenic disturbances.This study confirms that land-cover changes are among serious threats for heritage conservation,with heritage in some countries wherein the need to address this threat is most crucial,and the proposed spatiotemporal monitoring approach is recommended.

Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally small.The remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and reservoirs.However,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and reservoirs.In this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study areas.In the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and FLAASH.In the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV data.Based on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were discussed.It was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward trend.In the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

Further promotion of sustainable development goals using science,technology,and innovation

In 2015,193 United Nations(UN)member states unanimously adopted the 2030 Agenda for Sustainable Development.The 17 sustainable development goals(SDGs)drafted consist of 169 targets with over 230 indicators to be achieved by the year 2030.

Contribution of UNESCO designated sites to the achievement of Sustainable Development Goals

UNESCO designated sites(UDSs),including World Heritage Sites(WHSs),Biosphere Reserves(BRs)and Global Geoparks(GGPs),are a collection of focal spots deemed to be holding significant value for sustainable development within diverse environmental,social,and economic contexts.1 However,there is a clear lack of understanding and acknowledgement of UDSs’value and their specific contributions in policy-making related to development.This is especially true of the 17 Sustainable Development Goals(SDGs)that are part of the UN’s 2030 Agenda.

Assessing changes in nighttime lighting in the aftermath of the Turkey-Syria earthquake using SDGSAT-1 satellite data

On February 6,2023,a powerful earthquakewith amagnitude of 7.8MW struck southeastern Turkey and northwestern Syria.The epicenter of the earthquake was in Gaziantep,Turkey.The earthquake was followed by a devastating aftershock 9 h later,which registered a magnitude of 7.5 MW and which had an epicenter located 100 km farther north in Kahramanmaras,Turkey.Thousands of subsequent tremors and aftershocks since the initial earthquake have further exacerbated the impacts on lives and infrastructure,resulting in more than 50,000 deaths and damage to hundreds of thousands of buildings in the region.

A new denoising method for photon-counting LiDAR data with different surface types and observation conditions

Spaceborne photon-counting LiDAR is significantly affected by noise,and existing denoising algorithms cannot be universally adapted to different surface types and topographies under all observation conditions.Accordingly,a new denoising method is presented to extract signal photons adaptively.The method includes two steps.First,the local neighborhood radius is calculated according to photons’density,then thefirst-step denoising process is completed via photons’curvature feature based on KNN search and covariance matrix.Second,the local photonfiltering direction and threshold are obtained based on thefirst-step denoising results by RANSAC and elevation frequency histogram,and the local dense noise photons that thefirst-step cannot be identified are further eliminated.The following results are drawn:(1)experimental results on MATLAS with different topographies indicate that the average accuracy of second-step denoising exceeds 0.94,and the accuracy is effectively improves with the number of denoising times;(2)experiments on ICESat-2 under different observation conditions demonstrate that the algorithm can accurately identify signal photons in different surface types and topographies.Overall,the proposed algorithm has good adaptability and robustness for adaptive denoising of large-scale photons,and the denoising results can provide more reasonable and reliable data for sustainable urban development.

A hyperspectral detection model for permeability coefficient of debris flow fine-grained sediments, Southwestern China

Fine-grained sediments are Quaternary sediments with grain sizes of not more than 2 mm.They startfirst when meeting water,their stability is related to the initial water volume triggering debrisflow,and thus plays an important role in debrisflow hazards early warning.The permeability coefficient is the inter-controlled factor offine-grained sediment stability.However,there is no hyperspectral model for detecting thefine-grained sediment permeability coefficient in large areas,which seriously affects the progress of debrisflow hazards early warning.Therefore,it is of great significance to establish a hyperspectral detection model for the permeability coefficient offine-grained sediments.Taking Beichuan County,Southwestern China as the case,a permeability coefficient hyperspectral detection model was established.The results show that eight bands are sensitive to the permeability coefficient with correlation coefficient(R)of 0.6343.T-test on the model shows that P-a values for sensitive bands are all less than 0.05,indicating the established model has a good prediction ability with a precision of 85.83%.These sensitive bands also indicate the spectral characteristics of the permeability coefficient.Therefore,it provides a scientific basis forfine-grained sediment stability detection in large areas and lays a theoretical foundation for debrisflow hazards’early warning.

A point cloud segmentation method for power lines and towersbased on a combination of multiscale density features andpoint-based deep learning

The point segmentation of power lines and towers aims to use unmanned aerial vehicles(UAVs)for the inspection of power facilities,risk detection and modelling.Because of the unclear spatial relationship between the point clouds,the point segmentation of power lines and towers is challenging.In this paper,the power line and tower point datasets are constructed using Light Detection and Ranging(LiDAR)and a point segmentation method is proposed based on multiscale density features and a point-based deep learning network.First,the data are blocked and the neighbourhood is constructed.Second,the point clouds are downsampled to produce sparse point clouds.The point clouds before and after sampling are rotated,and their density is calculated.Next,a direct mapping method is selected to fuse the density information;a lightweight network is built to learn the features.Finally,the point clouds are segmented by concatenating the local features provided by PointCNN.The algorithm performs effectively on different types of power lines and towers.The mean interaction over union is 82.73%,and the overall accuracy can reach 91.76%.This approach can achieve the end-to-end integration of segmentation and provide theoretical support for the segmentation of large scenic point clouds.

Spatiotemporal variation of cyanobacterial harmful algal blooms in China based on literature and media information

Cyanobacterial harmful algal blooms(CyanoHABs)in inland waters are now among the most pressing environmental issues worldwide,especially in China.Satellite remote sensing has limitations in monitoring CyanoHABs in small water bodies due to spatial and temporal resolution limitations.While literature and news media have the potential to supplement satellite remote sensing in monitoring CyanoHABs,they have currently not received sufficient attention.In this study,we combined information on the distributions of CyanoHABs from literature and media for the first time to comprehensively assess the spatiotemporal variation in CyanoHABs in China.We collected,cleaned,validated,and organized data from literature and media on CyanoHABs in China,resulting in the establishment of a comprehensive database on CyanoHABs in China's inland waters(ChinaCyanoDB)covering 198 water bodies,525 records for 1950-2021.The majority of water bodies with CyanoHABs(CyanoWaters)are located in the eastern China,mainly concentrated in the middle and lower Yangtze region,with a clear upward trend in their number over the last four decades.The ChinaCyanoDB and analytical results can provide valuable data support for monitoring and managing CyanoHABs in China while the database construction method may also be applied to other countries and regions.

Cloud-based storage and computing for remote sensing big data:a technical review

The rapid growth of remote sensing big data(RSBD)has attracted considerable attention from both academia and industry.Despite the progress of computer technologies,conventional computing implementations have become technically inefficient for processing RSBD.Cloud computing is effective in activating and mining large-scale heterogeneous data and has been widely applied to RSBD over the past years.This study performs a technical review of cloud-based RSBD storage and computing from an interdisciplinary viewpoint of remote sensing and computer science.First,we elaborate on four critical technical challenges resulting from the scale expansion of RSBD applications,i.e.raster storage,metadata management,data homogeneity,and computing paradigms.Second,we introduce state-of-the-art cloud-based data management technologies for RSBD storage.The unit for manipulating remote sensing data has evolved due to the scale expansion and use of novel technologies,which we name the RSBD data model.Four data models are suggested,i.e.scenes,ARD,data cubes,and composite layers.Third,we summarize recent research on the application of various cloud-based parallel computing technologies to RSBD computing implementations.Finally,we categorize the architectures of mainstream RSBD platforms.This research provides a comprehensive review of the fundamental issues of RSBD for computing experts and remote sensing researchers.

Remotely sensed big data for the oceans and polar regions

The oceans,which account for 71%of the Earth’s area,and the polar regions,the largest cold source on Earth,jointly play crucial roles in energy exchange and circulation,and in climate change(e.g.McGuire,Chapin,Walsh,&Wirth,2006).In particular,against the background of global climate change,both the Arctic and Antarctic are experiencing profound changes(Bracegirdle,Connolley,&Turner,2008;Jeffries,Overland,&Perovich,2013),and the inter-actions between the oceans,polar regions and the atmosphere are closer than ever.Remote sensing has become one of the main research tools in ocean and polar studies(Lubin,Ayres,&Hart,2009).At the same time,the amount of acquired data is undergoing explosive growth(Ma et al.,2015),thus leading oceanography into the era of“big data”.

Sentinel-1 EW mode dataset for Antarctica from 2014–2020 produced by the CASEarth Cloud Service Platform

Antarctica plays an important role in research on global change,and its unique geography,ocean,climate,and environment pro-vide an ideal place for humankind to understand Earth’s evolution.Remote sensing provides an effective means to monitor and observe large-scale processes on the continent.Synthetic aperture radar(SAR)in particular provides the capability for all-weather Earth observation.The Sentinel-1A and Sentinel-1B SAR satellites have ideal ground coverage and imaging frequency for observing Antarctica.This study developed a dataset of 69,586 Sentinel-1 EW mode satellite images of the Antarctic ice sheet from October 2014 to December 2020.The dataset was processed with the European Space Agency Sentinel Application Platform(SNAP)and a Python batch scheduling tool on the Big Earth Data Cloud Service Platform of the Chinese Academy of Sciences Big Earth Data Science Engineering Program(CASEarth).Several data processing operations were implemented to process the raw dataset,including radiometric calibration,invalid edge removal,geocoding,data reprojection to an Antarctic projection,data compression to TIFF format,and construction of image pyramids.The dataset is avail-able at http://www.doi.org/10.11922/sciencedb.j00076.00085.

A lake ice phenology dataset for the Northern Hemisphere based on passive microwave remote sensing

Lake ice phenology(LIP)is an essential indicator of climate change and helps with understanding of the regional characteristics of climate change impacts.Ground observation records and remote sensing retrieval products of lake ice phenology are abundant for Europe,North America,and the Tibetan Plateau,but there is a lack of data for inner Eurasia.In this work,enhanced-resolution passive microwave satellite data(PMW)were used to investigate the Northern Hemisphere Lake Ice Phenology(PMW LIP).The Freeze Onset(FO),Complete Ice Cover(CIC),Melt Onset(MO),and Complete Ice Free(CIF)dates were derived for 753 lakes,including 409 lakes for which ice phenology retrievals were available for the period 1978 to 2020 and 344 lakes for which these were available for 2002 to 2020.Verification of the PMW LIP using ground records gave correlation coefficients of 0.93 and 0.84 for CIC and CIF,respectively,and the corresponding values of the RMSE were 11.84 and 10.07 days.The lake ice phenology in this dataset was significantly correlated(P<0.001)with that obtained from Moderate Resolution Imaging Spectroradiometer(MODIS)data-the average correlation coefficient was 0.90 and the average RMSE was 7.87 days.The minimum RMSE was 4.39 days for CIF.The PMW is not affected by the weather or the amount of sunlight and thus provides more reliable data about the freezing and thawing process information than MODIS observations.The PMW LIP dataset pro-vides the basic freeze-thaw data that is required for research into lake ice and the impact of climate change in the cold regions of the Northern Hemisphere.The dataset is available at http://www.doi.org/10.11922/sciencedb.j00076.00081.