Spatial Data Research Articles

DKK1+ tumor cells inhibited the infiltration of CCL19+ fibroblasts and plasma cells contributing to worse immunotherapy response in hepatocellular carcinoma.

Intra-tumor immune infiltration plays a pivotal role in the interaction with tumor cells in hepatocellular carcinoma (HCC). However, its phenotype and related spatial structure remained elusive. To address these limitations, we conducted a comprehensive study combining spatial data (38,191 spots from eight samples) and single-cell data (56,022 cells from 20 samples). Our analysis revealed two distinct infiltration patterns: immune exclusion and immune activation. Plasma cells emerged as the primary cell type within intra-tumor immune clusters. Notably, we observed the co-location of CCL19+ fibroblasts with plasma cells, which secrete chemokines and promote T-cell activation and leukocyte migration. Conversely, in immune-exclusion samples, this co-location was primarily observed in the adjacent normal area. This co-localization correlated with T cell infiltration and the formation of tertiary lymphoid structures, validated by multiplex immunofluorescence conducted on twenty HCC samples. Both CCL19+ fibroblasts and plasma cells were associated with favorable survival outcomes. In an immunotherapy cohort, HCC patients who responded favorably exhibited higher infiltration of CCL19+ fibroblasts and plasma cells. Additionally, we observed the accumulation of DKK1+ tumor cells within the tumor area in immune-exclusion samples, particularly at the tumor boundary, which inhibited the infiltration of CCL19+ fibroblasts and plasma cells into the tumor area. Furthermore, in immune-exclusion samples, the SPP1 signaling pathway demonstrated the highest activity in communication between tumor and immune clusters, and CCL19-CCR7 played a pivotal role in the self-communication of immune clusters. This study elucidates immune exclusion and immune activation patterns in HCC and identifies relevant factors contributing to immune resistance.

Spatial-Temporal Distribution of 12-Year Periodontal Disease Prevalence in a Large Population Using Geographical Information Systems: A Longitudinal Study.

Periodontal diseases, commonly linked to dental biofilm and affecting adults, were studied using Geographic Information Systems (GIS) and Kernel Analyses with epidemiological data. This paper presents a hybrid method for use in epidemiological studies by evaluating the spatiotemporal distribution of disease prevalence. This study analy ed 47,757 patients from the Department of Periodontology out of 662,351 visitors to University Faculty of Dentistry (2012 to July 2023). The central districts of Kayseri in Turkey were selected as the study areas. Periodontitis prevalence was assessed through radiographic evidence and clinical examination. Point-based location data, including gender, age, and disease type, matched household data, creating building-based spatial data. Kernel Density (KD) and Average Nearest Neighbor (ANN) analyses examined patient concentration and disease types in specific regions. Accordingly, standard deviation ellipses were prepared by year to assess the spatial changes in the regions where patients resided. The study found higher periodontitis prevalence in males, increasing with age, while gingivitis decreased. After 2017, periodontitis prevalence notably declined. Location-based data exhibited clustering in patient distribution. KD maps showed similar patient distributions over the years, with more applications from areas closer to the Faculty of Dentistry. The spatial distribution of the patients applying has remained consistent over the last 5 years. Through GIS, KD maps reveal the spatial-temporal distribution of periodontitis patients. This aids in identifying high-prevalence regions and guiding strategic healthcare facility placement. Implementing preventive programs in high-demand areas, particularly in family health centers (local health facilities), can reduce community-wide periodontal disease prevalence.

Social-ecological system trajectories of peri-urban watersheds based on a spatial analysis of vulnerability components: A case study in Mexico City, 1999-2039.

Urban periphery watersheds play a crucial role in providing diverse ecosystem services, especially hydrological services (HES), for society at different temporal and spatial scales; moreover, local populations directly influence ecosystem functionality through their decisions and actions. The interactions between social and ecological factors create social-ecological systems (SESs), whose trajectories continuously change in response to internal factors such as land use cover change (LUCC) and external factors such as climate change (CC). This situation influences the vulnerability of SESs in terms of exposure, sensitivity and adaptation capacity. In this study, the social-ecological vulnerability (SEV) of the periphery of Mexico City was investigated based on the Collaborative Protocol for Ecosystem Services Assessment and Social-ecological Vulnerability Mapping (ECOSER) and a quantitative method approach. For this purpose, spatial analysis was performed using the ecological and social spatial data for LUCC tendencies calculated for 1999-2019 and projected for short-term CC scenarios and using LUCC calculated for 2039 in trend-based (TREND) and restrictive (REST) scenarios. The results reveal that increases in the SEV in 2039 will be related to important decreases in the HES; furthermore, the REST scenario suggests decreases in the SEV due to decreases in the HES, assuming that environmental public policy instruments will be preserved in this region. The present work aims to contribute to decision-making for HES preservation at local and regional scales and to help develop adaptation strategies under LUCC and CC scenarios.

Riemannian Manifold-Aided Data-driven Diagnosis of Photovoltaic Hot Spots

Abstract This paper proposes a novel data-driven approach for hot-spot fault detection in photovoltaic (PV) modules, utilizing a curved Riemannian manifold (RM) to characterize the sample space. The proposed method detects hot spots in PV systems by mining geometric features in high-dimensional data. The RM-based method combines the geometric properties of Riemannian manifolds for fault detection. In addition, it also provides information about the severity of the hot spots. The proposed method has three main advantages:
1) it is capable of actively learning fault characteristics and can be applied to diagnose various degrees of hot spots;
2) unlike other data-driven methods, the proposed method considers non-Euclidean spatial data features, which further improves the accuracy of the method and reduces the false alarm rate (FAR);
3) it does not rely on mathematical models or expert knowledge, making it able to meet the real-time monitoring needs of actual PV systems.
The effectiveness and superiority of the proposed approach are verified through 12 sets of hot spots tests conducted on a PV experimental platform.

Analisis Potensi Kawasan Pengembangan Lahan Pertanian Tanaman Padi Sawah di Kabupaten Tanah Datar

This study aims to determine 1) the level of suitability of wet-rice land on food agricultural land in Tanah Datar Regency, 2) existing land cover in Tanah Datar Regency and 3) potential wet-rice land development areas in Tanah Datar Regency. The method used is a descriptive approach with quantitative research. Data was processed using fuzzy logic, supervised classification, and spatial data analysis. The results of this study show 1) Tanah Datar Regency has 3 levels of land suitability with an index value of 0-0.59 which is classified at the level of unsuitable to less suitable for rice paddy agricultural crops, with an area of 7,583.9 Ha with a percentage of 5.5%. The second category of land suitability with an index value of 0.6-0.79 is classified as moderately suitable to suitable, with an area of 98,955.7 Ha with a percentage of 71.8%. The third category of land suitability with an index value of 0.8-1.0 which is classified as moderately - highly suitable for wetland agricultural crops, with an area of 31,267.7 Ha with a percentage of 22.7% of the total area of the study site. 2) Analysis of existing land cover using Landsat 8 satellite images in 2023 in Tanah Datar District shows that the structure of land cover in 2023 is as follows: dense vegetation cover covering 93,385.5 Ha with a percentage of 67.9%, followed by agricultural land covering 35,630.6 Ha with a percentage of 25.8%, settlements covering 1,828.3 Ha with a percentage of 1.4%, open land covering 137.6 Ha with a percentage of 0.09%, and waters.

Development of methodology for calculating flooded area and flood volume in small urban areas based on unmanned aerial vehicle images

Climate change has intensified flooding and increased localized torrential rainfalls, leading to disasters such as landslides, infrastructure collapse, and urban floods. The extent and accuracy of flood damage information significantly impact recovery processes. While previous studies primarily utilized satellite and aerial imagery for broad flood assessments, they often lacked the precision needed for accurate damage analysis. This study addresses the gap between rapid assessment needs and precise damage quantification in flood inundation analysis. This research introduces a novel image-based investigation approach to enhance the speed and accuracy of flood inundation assessment. By leveraging unmanned aerial vehicles (UAVs) and image-based spatial data technology, aerial images of flooded areas are rapidly captured to construct three-dimensional disaster site terrain information. The proposed methodology employs advanced techniques in aerial photography, image processing, and geographic analysis to quantitatively analyze flood inundation scale using only aerial images and geographic information systems (GIS). The research yielded a calculated flood inundation area of 3,847.36 m2 and a flood volume of 13,895.13 m3. This methodology complements existing flood inundation assessment techniques and has the potential to significantly improve disaster management efforts by providing rapid, accurate, and actionable data for decision-makers.

Enhancing Oil and Gas Exploration and Development: The Role of Remote Sensing

The oil and gas industry are getting weaker in terms of exploration and development; it demands a new generation of game-changing solutions as far as resource extraction is concerned with environmental footprints. Remote sensing technology has become an essential facilitator in this mission by giving detailed spatial data and information to support decisions. This article looks at the diverse ways in which remote sensing is used to contribute to oil and gas exploration, from geological mapping and resource assessment through environmental monitoring to regulatory compliance. Using satellite, airborne and geophysical data to identify the best drilling locations, conduct hydrocarbon potential assessment and monitor any environmental consequences is possible with modern technology its need for ground surveys and exploratory drilling. Remote sensing is also playing a key role in exploration activity and EOR by monitoring surface deformation. In the end, use of remote sensing does more than just make operations easier and lowers operational costs in the oil and gas industry and supports environmentally and risk mitigation. Keywords: Oil and Gas Exploration, Geological Mapping, Environmental Monitoring, Drilling Optimization, Remote sensing

Risk Analysis of Groundwater Contaminant in Rural Areas Using Spatial Distribution

Background: Water is a very important material for the life of creatures in nature and its function for life cannot be replaced by other compounds. Groundwater is an important resource for humans, especially in rural areas that depend on Groundwaters as the main source of clean water. However, the quality of groundwater in many areas has decreased due to pollution from human activities, such as agriculture and industry. Method: This study aims to analyse the level of pollution risk of groundwaters in rural areas of Masama District, Banggai Regency using descriptive quantitative methods. Spatial analysis of pollution risk was conducted on 516 Groundwaters selected as samples. Spatial data is collected by recording the location of each well using GPS, which is then used to create a spatial map. The pollution risk analysis is based on the Sanitation Inspection results, using 11 key indicators. Result: The results showed that 4 Groundwaters (0.78%) were in the very high pollution risk category, 115 Groundwaters (22.29%) were in the high-risk category, 204 Groundwaters (39.53%) were classified as medium risk, and 193 Groundwaters (37.40%) had low pollution risk. The results indicate that most Groundwaters in the study area are at moderate to high risk. Groundwaters with high and very high risk are dominated by Groundwaters located in Minang Andala, Purwo Agung and Kembang Merta villages, which are agricultural areas with sufficient area and most of the people have livestock around their homes which could potentially be a source of contaminants. Conclusion: This study shows that the majority of Groundwaters in the study area have a moderate to high risk of pollution, with high and very high-risk categories found in villages that have agricultural and livestock activities. This emphasises the need for more intensive groundwater management and protection measures.

PEMBUATAN PETA DESA AIR MERAH KECAMATAN CURUP TENGAH KABUPATEN REJANG LEBONG BENGKULU

Maps are complex data sets containing information on the location and objects of an area. Maps can show the distribution of various things on Earth, such as settlement patterns, mountains and others. ArcGIS is software that can be used in making village maps. Air Merah village is a village in Curup Tengah sub-district, Bengkulu, which until now has not had complete mapping for the area. The purpose of this service is to help present village data that can be accessed as spatial data information in planning development progress in Air Merah Village. Community service is carried out in stages of visiting the village head's office for administrative processes, conducting discussions with the team, collecting data through field surveys and interviews, making maps, and discussing the results of thematic maps, as well as holding a presentation to discuss the results of the map and handing over map printouts to village officials. This service activity was attended by lecturers, partners and students who were involved in all series of services both in the field and discussions, so as to produce village information maps. Based on the results of field surveys and modeling, Air Merah Village has an area of ± 10.7789 km2 and is located at latitude longitude -3.4801, 102.556365. Map making has received a positive response from the surrounding community and is expected to be able to provide location information and improve the inventory of Air Merah Village.

A framework and pilot study for assessing usability of flood data portals for interdisciplinary research

There is a lack of datasets to study the climate and human outcomes nexus. There are many flood data portals due to recent improvements in flood identification using satellites, providing opportunities to study the human impacts. The development of these portals is rapid and there is currently no standard for evaluating their usability for interdisciplinary research. This paper addresses this important data gap. We put forth a usability framework that includes data availability, approaches to flood identification, alignment, velocity, variety, and user feasibility aspects. We piloted it through an in-depth review and user survey of NASA Worldview (NW), Global Flood Awareness System (GloFAS), Global Flood Monitoring System (GFMS), Global Surface Water Explorer (GSWE), and Dartmouth Flood Observatory (DFO) GSWE and GloFAS were rated most favorably. Respondents had discrepancies in their opinions on the clarity of the goals and platform accessibility for GFMS, DFO, and NW, and in data and visualization quality for all portals. Historical data and measures of flood recurrence and other characteristics are needed. Flood data products should be provided in multiple formats, aggregated by sub-national boundaries, with mechanisms that delineate incomplete or unreliable data. Flood data portals should include interdisciplinary research as part of their mission. Their longevity and maintenance should be secured to preserve these important data sources for future research. This framework can be adapted and used to enable interdisciplinary spatial and survey data linkages.

An Empirical Line approach for Agrowing Camera Aerial Images of inland waters based on exponential fit and in situ water measurements

Abstract. With the advancement of technology in the area of remote sensing, monitoring the Earth's surface using multispectral cameras attached to unmanned aerial vehicles (UAV) has become promising. However, in many Earth observation applications, it is needed to make compatible the spatial data of images captured by high spatial resolution multispectral sensors with the spectral response of targets on the Earth's surface. This relation is obtained through a radiometric calibration. The empirical line method is commonly used to calibrate the spectral bands of sensors. Thus, applications of this method using linear fit have retrieved negative values in water bodies. So that, attempting different adjustments, as well different reference targets, can solve this issue. In this study, the water quality of small bodies of water was analysed using a Agrowing multispectral camera, which derived negative values when applying linear fit. The aim of this study, therefore, was to fit a radiometric calibration based on empirical line method for Agrowing camera in inland water applications. Besides of standard reference targets, water samples also were attempted because showed a lower radiance response than the darkest (black) calibration target. Two empirical line methods were applied to convert the digital number (DN) from the Agrowing images into remote sensing reflectance (Rrs): linear and exponential. The exponential method showed to be more appropriate, with greater accuracy, unlike the linear method.

Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models

Abstract. This study addresses the challenge of estimating above-ground biomass (AGB) in the Amazon rainforest by developing a reference geographical database, which provides the ground truth, and comparing the relative importance of using Synthetic Aperture Radar (SAR) and optical remote sensing data to automatically infer AGB. In the experiments reported in this article, we assessed how those two remote sensing data sources impact the accuracy of AGB estimates produced by regression models built with Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The research involved compiling a comprehensive database from many available forest inventories, integrating parcel- and tree-level data to enable precise biomass estimation. The methodology included setting up a spatial data analysis environment, standardizing data, and implementing an experimental protocol with feature selection and leave-one-out cross-validation. The results demonstrate that both kinds of data, i.e., SAR and optical, and their combination can be used for estimating AGB, providing valuable insights for forest management and climate change mitigation efforts. The reference database is available upon request to the corresponding authors.

Landslide Studies in the Context of Disaster Management in Bangladesh—A Systematic Literature Review

Landslides and their resulting impacts on property and human life have become an ongoing challenge in the hilly regions of Bangladesh. This study aims to systematically review diverse landslide studies in Bangladesh, particularly focusing on landslide disaster management (LDM) from 2008 to 2023, encompassing the pre-disaster, syn-disaster, and post-disaster phases. Several key attributes of landslide studies were considered, including general trends, data types, study scales, contributing factors, methodologies, results, and validation approaches, to investigate challenges and subsequently identify research gaps. This study evaluated 51 research articles on LDM using a systematic literature review (SLR) technique that adhered to the Preferred Reporting Item for Systematic Reviews and Meta-Analyses (PRISMA) framework. Our finding revealed that articles on LDM were dominated by the pre-disaster (76%) and the syn-disaster phases (12%), with the post-disaster phase (12%) receiving equal attention. The SLR revealed a growing number of studies since 2020 that used data-driven methods and secondary spatial data, often focused on medium-scale analyses (district level) that, however, often lacked field-based validation. From the factors examined in various landslide studies, topographical and hydrological factors were found to be the most significant attributes in assessment. This study identified key challenges, such as insufficient landslide inventories including poor site accessibility and a lack of high-resolution geological, soil, and rainfall data. It also highlighted critical research gaps, including the need for advanced technologies in susceptibility mapping for national hazard atlas, the investigation of underexplored causative factors, effective early warning systems, detailed post-event characterization, health impact assessment, risk-sensitive land use planning, and interactive web portals for landslide prone areas. This study would thus aid researchers in understanding the depth of existing knowledge and provide insights into how landslides fit into broader disaster management frameworks, facilitating interdisciplinary approaches.

County-Level Spatiotemporal Dynamics and Driving Mechanisms of Carbon Emissions in the Pearl River Delta Urban Agglomeration, China

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods.

Dry mass grassland estimation using UAV ultra-wide RGB images

Abstract. Dry mass is an important parameter to optimise grassland management. Traditionally, dry mass values are estimated manually by cutting, drying, and weighing vegetation samples. In large areas of cultivation, this becomes a time-consuming and costly activity. In recent years, many researchers have studied different sensors embedded in Unmanned Aerial Vehicles (UAV) to collect spatial data and estimate biomass using machine learning algorithms for forest and agricultural applications. However, there needs to be more research dealing with estimating production indices for pasture, especially in Brazil, as stated. This study evaluates the feasibility of using the GoPro wide-angle RGB camera on UAVs (Unmanned Aerial Vehicles) to estimate the dry mass of pastures. Different data analysis methods were compared, including the combination of vegetation indices (VIs) values and three-dimensional metrics (3D) extracted from the Canopy Height Model (CHM): all metrics (ALL), three VIs plus four 3D metrics (VI3 + CHM4) and only 3D metrics. Random Forest (RF) machine learning algorithm was used to estimate dry mass. The best results were obtained when merging all the variables from the two flight campaigns, with a coefficient of determination (R2) of 0.80 for the model and a Pearson Correlation Coefficient (PCC) of 0.85 for validation, with a Root Mean Square Error (RMSE%) of 20.5%. In summary, using RGB sensors embedded in UAVs is a promising technique for estimating farm grazing parameters.

Considerations for enhancing participation and data accuracy in geospatial research in rural areas: experiences with PGIS in northern Malawi

ABSTRACT Rural environments are experiencing rapid changes that must be explored to understand, enhance, and facilitate positive changes and adapt to detrimental changes. However, the information researchers can obtain about the environment to identify effective management strategies for rural resources is hindered by several factors. Participatory geospatial research presents an approach that integrates local voices to map the facts and values of rural people and represent environmental changes. Here, we draw on more than six years of participatory geospatial research in rural northern Malawi to identify and present various considerations that participatory geospatial researchers and planners should be mindful of when working with rural people to enhance participation in research and improve spatial data accuracy. Based on experiences using various research methods and activities applied in several transdisciplinary collaborative research projects, we posit that rural geospatial researchers should keenly consider i) ethical issues concerning data collection, analysis, and representation, e.g. taboos and sacred spaces, ii) integrating local spatial ecological knowledge of people about the environment, and iii) economic conflicts and gender dynamics that tend to disempower and limit participation in research and affect data quality. Considering these would build rapport between participants and researchers to facilitate active participation and data accuracy.

Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images

Aerial images have distinct characteristics, such as varying target scales, complex backgrounds, severe occlusion, small targets, and dense distribution. As a result, object detection in aerial images faces challenges like difficulty in extracting small target information and poor integration of spatial and semantic data. Moreover, existing object detection algorithms have a large number of parameters, posing a challenge for deployment on drones with limited hardware resources. We propose an efficient small-object YOLO detection model (ESOD-YOLO) based on YOLOv8n for Unmanned Aerial Vehicle (UAV) object detection. Firstly, we propose that the Reparameterized Multi-scale Inverted Blocks (RepNIBMS) module is implemented to replace the C2f module of the Yolov8n backbone extraction network to enhance the information extraction capability of small objects. Secondly, a cross-level multi-scale feature fusion structure, wave feature pyramid network (WFPN), is designed to enhance the model’s capacity to integrate spatial and semantic information. Meanwhile, a small-object detection head is incorporated to augment the model’s ability to identify small objects. Finally, a tri-focal loss function is proposed to address the issue of imbalanced samples in aerial images in a straightforward and effective manner. In the VisDrone2019 test set, when the input size is uniformly 640 × 640 pixels, the parameters of ESOD-YOLO are 4.46 M, and the average mean accuracy of detection reaches 29.3%, which is 3.6% higher than the baseline method YOLOv8n. Compared with other detection methods, it also achieves higher detection accuracy with lower parameters.

The roles of habitat isolation, landscape connectivity and host community in tick-borne pathogen ecology.

Habitat loss and forest fragmentation are often linked to increased pathogen transmission, but the extent to which habitat isolation and landscape connectivity affect disease dynamics through movement of disease vectors and reservoir hosts has not been well examined. Tick-borne diseases are the most prevalent vector-borne diseases in the United States and on the West Coast, Ixodes pacificus is one of the most epidemiologically important vectors. We investigated the impacts of habitat fragmentation on pathogens transmitted by I. pacificus and sought to disentangle the effects of wildlife communities and landscape metrics predictive of pathogen diversity, prevalence and distribution. We collected pathogen data for four co-occurring bacteria transmitted by I. pacificus and measured wildlife parameters. We also used spatial data and cost-distance analysis integrating expert opinions to assess landscape metrics of habitat fragmentation. We found that landscape metrics were significant predictors of tick density and pathogen prevalence. However, wildlife variables were essential when predicting the prevalence and distribution of pathogens reliant on wildlife reservoir hosts for maintenance. We found that landscape structure was an informative predictor of tick-borne pathogen richness in an urban matrix. Our work highlights the implications of large-scale land management on human disease risk.

Strain mode measurement of asphalt pavements using distributed fibre optic sensor data

This paper proposes a method for measuring strain modes in asphalt pavements using distributed fibre optic strain sensor data. A processing algorithm based on cross power spectral density (CPSD) was developed to utilize high spatial resolution strain data to determine the CPSD peak frequency and corresponding relative amplitude. The proposed method was applied in both laboratory and field settings, comparing the results of distributed fibre optic cables with those of accelerometers or theoretical solutions to validate accuracy. Local heating of asphalt structures was used to test the sensitivity of the measured strain modes to changes in material properties. The results demonstrate that different fibre optic cables can provide peak frequencies comparable to traditional accelerometers (±0.6 Hz). Additionally, they offer relative amplitude with a resolution of 1 cm, achieving a modal assurance criterion exceeding 0.95 when compared to theoretical strain mode shapes. The method also sensitively reflects material property changes at different positions within the asphalt structure. This indicates that the proposed approach can effectively measure strain modes in asphalt structures, offering a more detailed understanding of the distribution of structural properties compared to traditional methods.

Prospects for the development of cartography through the integration of SLAM technology with GIS technology

Abstract. With the continued development of Simultaneous Localization and Mapping (SLAM) and Geographic Information Systems (GIS) technologies, their application scenarios have become increasingly complex. Combining these technologies can significantly enhance operational efficiency in challenging environments. This paper presents an analysis of existing cases where SLAM and GIS technologies have been integrated, demonstrating that such a merger facilitates the consolidation and complementarity of spatial data. This integration allows robots or systems to simultaneously utilize the global information provided by GIS and the dynamic local data captured by SLAM for a more comprehensive and detailed environmental analysis, which is highly beneficial for the field of cartography. Further research has developed a series of operational procedures for integrating SLAM and GIS, utilizing MATLAB as a tool. This study also reviews several existing technical challenges, including real-time performance and computational capacity, environmental complexity and dynamic changes, and multi-scale data processing, and proposes potential solutions. The paper concludes by predicting that the integration of SLAM and GIS will play a crucial role in areas such as smart city management and disaster emergency response, indicating that this research area will become a hot topic in future cartographic technology.