Spatial Information Application Research Articles

KGTS: Contrastive Trajectory Similarity Learning over Prompt Knowledge Graph Embedding

Trajectory similarity computation serves as a fundamental functionality of various spatial information applications. Although existing deep learning similarity computation methods offer better efficiency and accuracy than non-learning solutions, they are still immature in trajectory embedding and suffer from poor generality and heavy preprocessing for training. Targeting these limitations, we propose a novel framework named KGTS based on knowledge graph grid embedding, prompt trajectory embedding, and unsupervised contrastive learning for improved trajectory similarity computation. Specifically, we first embed map grids with a GRot embedding method to vigorously grasp the neighbouring relations of grids. Then, a prompt trajectory embedding network incorporates the resulting grid embedding and extracts trajectory structure and point order information. It is trained by unsupervised contrastive learning, which not only alleviates the heavy preprocessing burden but also provides exceptional generality with creatively designed strategies for positive sample generation. The prompt trajectory embedding adopts a customized prompt paradigm to mitigate the gap between the grid embedding and the trajectory embedding. Extensive experiments on two real-world trajectory datasets demonstrate the superior performance of KGTS over state-of-the-art methods.

Assessing the Efficacy of Pixel-Level Fusion Techniques for Ultra-High-Resolution Imagery: A Case Study of BJ-3A.

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3-0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios.

Air travel demand forecasting based on big data: A struggle against public anxiety.

It is of great significance to accurately grasp the demand for air travel to promote the revival of long-distance travel and alleviate public anxiety. The main purpose of this study is to build a high-precision air travel demand forecasting framework by introducing effective Internet data. In the age of big data, passengers before traveling often look for reference groups in search engines and make travel decisions under their informational influence. The big data generated based on these behaviors can reflect the overall passenger psychology and travel demand. Therefore, based on big data mining technology, this study designed a strict dual data preprocessing method and an ensemble forecasting framework, introduced search engine data into the air travel demand forecasting process, and conducted empirical research based on the dataset composed of air travel volume of Shanghai Pudong International Airport. The results show that effective search engine data is helpful to air travel demand forecasting. This research provides a theoretical basis for the application of big data mining technology and data spatial information in air travel demand forecasting and tourism management, and provides a new idea for alleviating public anxiety.

A MULTI-SCALE POINT CLOUDS SEGMENTATION METHOD FOR URBAN SCENE CLASSIFICATION USING REGION GROWING BASED ON MULTI-RESOLUTION SUPERVOXELS WITH ROBUST NEIGHBORHOOD

Abstract. Point clouds classification is the basis for 3D spatial information extraction and applications. The point-clusters-based methods are proved to be more efficient and accurate than the point-based methods, however, the precision of the classification is significantly affected by the segmentation errors. The traditional single-scale point clouds segmentation methods cannot segment complex objects well in urban scenes which will result in inaccurate classification. In this paper, a new multi-scale point clouds segmentation method for urban scene point clouds classification is proposed. The proposed method consists of two stages. In the first stage, to ease the segmentation errors caused by density anisotropy and unreasonable neighborhood, a multi-resolution supervoxels segmentation algorithm is proposed to segment the objects into small-scale clusters. Firstly, the point cloud is segmented into initial supervoxels based on geometric and quantitative constraints. Secondly, robust neighboring relationships between supervoxels are obtained based on kd-tree and octree. Furthermore, the resolution of supervoxels in the planar and low-density region is optimized. In the second stage, planar supervoxels are clustered into the large-scale planar point clusters based on the region growing algorithm. Finally, a mix of small-scale and large-scale point clusters is obtained for classification. The performance of the segmentation method in classification is compared with other segmentation methods. Experimental results revealed that the proposed segmentation method can significantly improve the efficiency and accuracy of point clouds classification than other segmentation methods.

Spatial Feature-Based Prioritization for Transmission of Point Cloud Data in 3D-Image Sensor Networks

A sensor network that connects multiple 3D image sensor devices using light detection and ranging (LIDAR) is effective for collecting spatial information to detect potential risks associated with people’s movements. However, due to the large volume and high frame rate of point cloud data obtained by LIDAR devices, under the condition of strictly limited bandwidth, some part of the data can go missing when detection is performed from point cloud data, causing a degradation of the accuracy. As the communication traffic volume of other applications increases, the communication available bandwidth for spatial information applications becomes insufficient. This paper proposes a scheme of spatial feature-based prioritization for the transmission of point cloud data with strictly limited communication bandwidth. It estimates how much each piece of point cloud data contributes to the accuracy improvement of a machine learning task for spatial prediction and assigns a higher score to those with a larger contribution. These scores prioritize the control for the transmission of point cloud data from sensor devices to an edge server. Feature selection with machine learning is helpful for estimating data importance. To improve the estimation accuracy, the proposed scheme complements missing entries when performing feature selection. We developed an indoor experimental environment and found that the prediction accuracy was sufficiently maintained compared to two benchmark schemes, random and heuristic, with limited communication bandwidth. Comparison with the perturbation method, which is a conventional method of feature selection, demonstrated the effectiveness to complement missing values in the proposed scheme.

Land Cover Classification with Multispectral LiDAR Based on Multi-Scale Spatial and Spectral Feature Selection

The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial–spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial–spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information’s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial–spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data.

Spatial Information and Performance Evaluation in Coprime Array

In this paper, the mutual information between the received signals and the source in the coprime linear array is investigated. In Shannon’s information theory, the mutual information is used to quantify the reduction in the priori uncertainty of the transmitted message. Similarly, the spatial information in the coprime array is the mutual information between direction of arrival (DOA), source amplitude, and received signals. Such information content is composed of two parts. The first part is DOA information, and the second one is scattering information. In a single source scenario, we derive the theoretical expression and its asymptotic upper bound of DOA information. The corresponding expression of scattering information is also formulated theoretically. Besides, the application of spatial information is discussed. We can obtain the optimal array configuration by maximizing the DOA information of the coprime array. Similarly, the information is also used to quantify the performance difference between the coprime array and uniform array. In addition, the entropy error is employed to evaluate the estimation performance based on spatial information. Numerical simulation of the information content confirms our theoretical analysis. The results in this paper have important guiding significance for the design of the coprime array in the actual environment.

Editorial for the Special Issue on Selected Papers from the “2019 International Symposium on Remote Sensing”

The 2019 International Symposium on Remote Sensing (ISRS-2019) took place in Taipei, Taiwan from 17 to 19 April 2019. ISRS is one of the distinguished conferences on the photogrammetry, remote sensing and spatial information sciences, especially in East Asia. More than 220 papers were presented in 37 technical sessions organized at the conference. This Special Issue publishes a limited number of featured peer-reviewed papers extended from their original contributions at ISRS-2019. The selected papers highlight a variety of topics pertaining to innovative concepts, algorithms and applications with geospatial sensors, systems, and data, in conjunction with emerging technologies such as artificial intelligence, machine leaning and advanced spatial analysis algorithms. The topics of the selected papers include the following: the on-orbit radiometric calibration of satellite optical sensors, environmental characteristics assessment with remote sensing, machine learning-based photogrammetry and image analysis, and the integration of remote sensing and spatial analysis. The selected contributions also demonstrate and discuss various sophisticated applications in utilizing remote sensing, geospatial data, and technologies to address different environmental and societal issues. Readers should find the Special Issue enlightening and insightful for understanding state-of-the-art remote sensing and spatial information science research, development and applications.

From silent knowledge to spatial information – Mapping blue growth scenarios for maritime spatial planning

Maritime Spatial Planning (MSP) is an effective tool for conciliating human activities and environmental values, building on spatial data and geoinformation technologies. However, socio-economic information is distinctly underrepresented in the rapidly growing supply of spatial information. The spatial distribution of current and future activities and opinions has traditionally been the silent information of scientists, local actors and the public. Moreover, future projections and policies exist in qualitative, non-spatial formats, incompatible with quantitative biophysical spatial data layers. This article aims at promoting the generation and application of spatial socio-economic information for the purposes of MSP. We examine one workflow of converting the socio-economic knowledge of individual experts to spatial data, and further to refined spatial knowledge. We illustrate how participatory mapping, data interpretation and core geocomputing methods may be used to generate data, and discuss the main issues related to their generation and use. The results suggest that participatory mapping can provide valuable data for the MSP process, helping in filling the gap of missing socio-economic information. The process is highly subjective: the presentation of background information, the framing of the questions and the interpretation of the spatial data may have notable influence on the generated information. Furthermore, both the technology of the data collection and applied analysis methods have distinct effects on spatial information and its validity.

Spatial Signature Algorithm (SSA): A New Approach in Countermeasuring XML Signature Wrapping Attack

This paper introduces a new approach in countermeasuring XML signature wrapping attack called the Spatial Signature Algorithm (SSA). The motivation for proposing the SSA approach is due to the limitation of the SOAP (Simple Object Access Protocol) in handling the XML signature wrapping attacks. A different strategy is to be planned in order to deter such attack without extensive computational expense. Spatial Signature Algorithm builds upon the notion of ratio signature that is recommended by a research in biotechnology. The research suggests the possibility of diagnosing a specific disease based on the idea of ratios, specifically on the comparative relationship between elements to detect the emergence of certain threats. Bridging this notion to security, the principle of using space and ratio to detect abnormality is extended to the application of spatial information and digital signature to detect and combat the XML wrapping signature attack.

GIS Based landform classification using digital elevation model: a case study from two river basins of Southern Western Ghats, Kerala, India

The main purpose of this study is to classify/characterize the landforms within a river basin using complex spatial information and GIS application to recognize and extort geomorphologic properties of Digital Elevation model with 30 m resolution. The landform classification is based on the Topographic position index (TPI) of the area under study—the Ithikkara and Kallada river basins, Southern Western Ghats, Kerala, India. The TPI generated was used for classifying the landscape to slope position index and landform classes. The slope position classes identified are Ridges/Hilltop/Canyon edge, Upper slope, Mid slope, Flat, Lower slope and Valley. The Landform classes includes (1) Canyons, deeply incised streams, (2) Midslope drainages, shallow valleys, (3) Upland drainages, headwaters, (4) U-shaped valleys, (5) Plains, (6) Open slopes, (7) Upper slopes, mesas, (8) Local ridges/hills in valleys, (9) Midslope ridges, small hills in plains and (10) Mountain tops, high ridges. 50.23% of the study area belongs to the slope position class—Ridges/Hilltop/Canyon edge. 30% of the study area is occupied by the landform class Canyons and deeply incised streams followed by Mountain tops and high ridges which covers 28.16%. The U shaped valleys represent 12.32% of the study area. The validation of landform classes is done by overlaying the stream network of the basin is into the landform classes, which is perfectly overlapping with the U shaped valleys of the landform classes. The classification of landform from the study can be used in applications related to precision agriculture, land use alteration studies where the most dominant factor is landform.

2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop

Among spatial information applications, SpatialHadoop is one of the most important systems for researchers. Broad analyses prove that SpatialHadoop outperforms the traditional Hadoop in managing distinctive spatial information operations. This paper presents a Two Dimensional Priority R-Tree (2DPR-Tree) as a new partitioning technique in SpatialHadoop. The 2DPR-Tree employs a top-down approach that effectively reduces the number of partitions accessed to answer the query, which in turn improves the query performance. The results were evaluated in different scenarios using synthetic and real datasets. This paper aims to study the quality of the generated index and the spatial query performance. Compared to other state-of-the-art methods, the proposed 2DPR-Tree improves the quality of the generated index and the query execution time.

DETECTING FLU OUTBREAKS BASED ON SPATIOTEMPORAL INFORMATION FROM URBAN SYSTEMS – DESIGNING A NOVEL STUDY

Abstract. This paper explores the application of real-time spatial information from urban transport systems to understand the outbreak, severity and spread of seasonal flu epidemics from a spatial perspective. We believe that combining travel data with epidemiological data will be the first step to develop a tool to predict future epidemics and to better understand the effects that these outbreaks have on societal functions over time. Real-time data-streams provide a powerful, yet underutilised tool when it comes to monitoring and detecting changes to the daily behaviour of inhabitants. In this paper, we describe and discuss the design of the geospatial project, in which we will draw upon data sources available from the Norwegian cities of Oslo and Bergen. Historical datasets from public transport and road traffic will serve as an initial indication of whether changes in daily transport patterns corresponds to seasonal flu data. It is expected that changes in daily transportation habits corresponds to swings in daily and weekly flu activity and that these differences can be measured through geostatistical analysis. Conceptually one could be able to monitor changes in human behaviour and activity in nearly true time by using indicators derived from outside the clinical health services. This type of more up-to-date and geographically precise information could contribute to earlier detection of flu outbreaks and serve as background for implementing tailor-made emergency response measures over the course of the outbreaks.

Development of Data Fusion Method Based on Topological Relationships Using IndoorGML Core Module

Geospatial datasets are currently constructed, managed, and utilized individually according to the spatial scale of the real world, such as the ground/surface/underground or indoor/outdoor, as well the particular purpose of the geospatial data used for location-based services. In addition, LBS applications use an optimal data model and data format according to their particular purpose, and thus, various datasets exist to represent the same spatial features. Such duplicated geospatial datasets and geographical feature-based GIS data cause serious problems in the financial area, compatibility issues among LBS systems, and data integration problems among the various geospatial datasets generated independently for different systems. We propose a geospatial data fusion model called the topological relation-based data fusion model (TRDFM) using topological relations among spatial objects in order to integrate different geospatial datasets and different data formats. The proposed model is a geospatial data fusion model implemented in a spatial information application and is used to directly provide spatial information-based services without data conversion or exchange of geometric data generated by different data models. The proposed method was developed based on an extension of the AnchorNode concept of IndoorGML. The topological relationships among spatial objects are defined and described based upon the basic concept of IndoorGML. This paper describes the concept of the proposed TRDFM and shows an experimental implementation of the proposed data fusion model using commercial 3D GIS software. Finally, the limitations of this study and areas of future research are summarized.

ISA 2016 workshop report

The series of ISA workshops started in 2009 to exchange original and cutting-edge research dedicated to indoor spatial awareness. As spatial information technologies and applications grow beyond their traditional boundaries, research on indoor space is increasingly attracting attention from academia and industry. Location Based Services, Geographic Information Systems, and Ubiquitous Computing Applications are all extending their coverage from outdoor to indoor spaces. Indoor space differs from outdoor space in several important aspects, including layout, topology, constraints, and so forth. High-quality research is required to create new theories and to acquire fresh experiences that can be applied to building integrated, seamless systems across spatial boundaries. This workshop is intended to bring together researchers, developers, and practitioners who work on different topics of indoor spatial awareness and to provide a forum for inspiring interdisciplinary discussions in all relevant aspects.

교육과 연구에 있어 FOSS4G의 활용에 대한 검토

그동안 정부는 공간정보산업에 있어서 국내 기술의 발전을 위해 많은 정책들을 수행하고 투자해 왔지만, 국내 기술의 실용화 및 국제화는 미진한 수준이다. 국내 기술의 발전에 기초가 되는 교육과 연구에 있어서도 외산 소프트웨어에 의존하는 경우가 많다. OSS는 이러한 독점 소프트웨어에 대한 의존을 벗어나는 대안으로 관심을 받아왔다. 본 연구에서는 최근에 공간정보산업분야에서 주목 받고 있는 FOSS4G와 관련한 해외의 주요 동향과 각국의 사례들을 검토하였다. 특히 OSS 성장의 기초가 되어온 교육과 연구 분야의 사례들을 검토하고, 이들을 통해 국내 공간정보의 발전을 위해 필요한 정책적인 함의를 분석하였다. Since the government has implemented the spatial information policies, there have been great investments and efforts made for the development of domestic technology. However, the practical application of spatial information and the degree of internationalization still leaves more to work to do. The demand of spatial information-related education, and the research and development still relies on the proprietary software from outside. Open Source Software (OSS) has been considered as an alternative for this reason. This study reviews the major trends and examples of FOSS4G in the world, which receives a special attention in the field of spatial information industry. In particular, we analyze the policy implication for the development of national spatial information by examining cases of education and research areas that have been the foundation of the growth of OSS.

건설현장 사전 환경조사를 위한 공간정보의 적용기법

건설공사에서 사전 환경조사는 시공 전 현장 조사 서비스뿐만 아니라, 계획 및 설계에서도 매우 중요한 요소이다. USN과 결합된 지형공간정보의 적용은 사전에 건설 현장의 진행 단계마다 조사 및 연구에 매우 경제적인 방법이기 때문에 건설 환경 계획 및 기본 설계의 건설을 위하여 매우 중요한 역할을 수행하여 불필요한 시행착오를 줄일 수 있다. 본 연구에서는 공간위치를 기반으로 환경 센서와 무선통신 기술을 융합하여 건설현장을 디지털 공간정보 환경으로 전환하고, 현장에 설치된 무선의 소형 환경 센서를 이용하여 각 유비쿼터스 센서에서 측정된 환경 데이터를 싱크 센서가 데이터 수집 처리하고 초소형 Tiny OS 기반의 PC 출력 이미지를 전송하는 방식으로 현재 진행되고 있는 건설 현장의 여러 환경정보를 3차원 지형공간에서 실시간으로 그 환경변화를 모니터링 할 수 있도록 하였다. The environmental survey in advance in the construction works is very important for planning and designing as well as the service of field survey before carrying out construction. The topographical application of spatial information coupled with USN is the very economical method for the survey and research every processing stage of construction field in advance. Therefore the execution of very important role for environmental planning and fundamental designing of construction reduces the unnecessary trial and error through the environmental survey in advance. In this research the environment of existent construction field is transformed to that of digital spatial information by fusing the sensor network with wireless technique on the base of spatial position. In addition, the sink sensor cumulates the environmental data measured from each USN sensor using small wireless environmental sensors installed at the construction site and changes of various environmental data at the present constructing site are able to be monitored at 3-D topographical space in real time by using the method for transmitting the image of PC output based on TinyOS.

EXPLORING THE APPLICATION OF VOLUNTEERED GEOGRAPHIC INFORMATION TO CATCHMENT MANAGEMENT: A SURVEY APPROACH

Abstract. The participation and engagement of grass-root level community groups and citizens for natural resource management has a long history. With recent developments in ICT tools and spatial technology, these groups are seeking a new opportunity to manage natural resource data. There are lot of spatial information collected/generated by landcare groups, land holders and other community groups at the grass-root level through their volunteer initiatives. State government organisations are also interested in gaining access to this spatial data/information and engaging these groups to collect spatial information under their mapping programs. The aim of this paper is to explore the possible utilisation of volunteered geographic information (VGI) for catchment management activities. This research paper discusses the importance of spatial information and spatial data infrastructure (SDI) for catchment management and the emergence of VGI. A conceptual framework has been developed to illustrate how these emerging spatial information applications and various community volunteer activities can contribute to a more inclusive spatial data infrastructure (SDI) development at local level. A survey of 56 regional NRM bodies in Australia was utilised to explore the current community-driven volunteer initiatives for NRM activities and the potential of utilisation of VGI initiatives for NRM decision making process. This research paper concludes that VGI activities have great potential to contribute to SDI development at the community level to achieve better natural resource management (NRM) outcomes.

모바일 공간정보산업 활성화를 위한 정책 제안 연구

The application area of spatial information such as urban planing, facility management, traffic service are expanded to new service areas such as environmental pollution, securing resources, u-City. In particular, the increased importance of spatial information application in mobile environment based on smart phone, a mobile spatial data infrastructure and related strategy suggestions to provide mobile spatial information service is required. In this paper, we derived strategy suggestions to foster the next generation strategic industry and proposed the role of public sector, the development of mobile spatial information services, the construction of mobile spatial information infrastructure and the stepwise strategies.

基于Landsat TM的热带精细地物信息提取的模型与方法——以海南岛为例

应用遥感技术进行精细地物信息提取是研究生态系统结构、过程和功能的重要手段之一。由于热带地区生态系统复杂,为精细地物信息提取带来很大的不确定性,极易产生"同物异谱"、"同谱异物"的现象。研究以地处热带地区的海南岛精细地物遥感信息提取为例,在综合分析典型地物光谱特征、空间分布、斑块形状等基础上,构建和优化了水陆指数WLI(Water and Land differing Index)、乔灌草指数GSI(Grass and Shrub differing Index)、旱地-沙地指数SSI(Field and Sand differing Index),并结合新型通用植被指数VIUPD(Vegetation Index of the Universal Pattern Decomposition Method)及DEM(Digital Elevation Model)等多源数据,提出基于决策树的面向对象遥感信息提取方法。该方法首先确定要提取的对象,明确对象类别与对象隶属关系,然后逐层逐项的提取天然林、橡胶林、浆纸林等地物信息。结果表明,综合提取的精度达88%,相比传统的监督分类方法精度(66%)提高22个百分点,精度明显提高。;The service functions of different ecosystems vary widely and include water conservation, soil and water conservation, and the maintenance of biological diversity. Feature extraction from images obtained from remote sensing technology is an important method used to study the structures, processes and functions of ecosystems. Tropical evergreen ecosystems are very complex because different surface features can share the same spectra, or similar surface features can produce different spectra, making information extraction difficult. Current methods used for information extraction have some problems including the lack of application of spatial information and multi-scale image segmentation, the lack of an effective model to extract homogeneous plaque, and the lack of multi-source data support. To address these problems we carried out this study using Landsat TM data. Information on complex ground features on Hainan Island, China, was extracted after comprehensive analysis of spectral characteristics, spatial distribution, patch shape and other characteristics of typical ground objects. We developed an information extraction method based on the decision tree model, and a set of remote sensing information extraction models. These extraction models included the land and water index WLI (Water and Land differing Index), the bushes and grass index GSI (Grass and Shrub differing Index), the dry field and sand index SSI (Field and Sand differing Index), a new vegetation index VIUPD (Vegetation Index based on the Universal Pattern Decomposition method), and the DEM (Digital Elevation Model). According to the classes and interrelationship of target objects, first we established flow charts and processes for sophisticated object-based information extraction based on the decision tree. At the same time, we identified and extracted the information on surface features, taking prior knowledge of the spatial distribution and texture characteristics of ground features into account, and utilized multi-source data (DEM, planning maps and other auxiliary data). Depending on the node order, first we separated areas of water and land. We then separated areas with buildings without vegetation coverage from areas with no buildings, and finally, using the VIUPD method, the DEM and the GSI, nine different surface features including natural forest, rubber trees, pulp and paper forest, paddy field, dry land, orchards, and sand, were classified. We evaluated the precision of the classification by comparing results with high resolution remote sensing images obtained by Quick Bird and SPOT, and 300 GPS positioned points confirmed by local technical staff. The accuracy of our method was 88%, compared with traditional supervised and non-supervised classification methods that have a maximum accuracy of 66%. The accuracy was therefore significantly improved. Based on the results of this study we concluded that the WLI, the GSI, the SSI, and the VIUPD method we developed performed well in the information extraction of complex features on Hainan Island. The decision tree-based object-oriented information extraction model combined with the above models was applicable and effective in the classification of complex surface features in a tropical evergreen ecosystem when the information extraction model was tailored for tropical areas. After validation, the accuracy of feature extraction was greatly improved compared with traditional classification methods. The thresholds and parameters of the model can be adjusted for different ecosystems; therefore, this method is a valuable and widely applicable new tool for information extraction.