MSC-YOLO:Improved YOLOv7 Based on Multi-Scale Spatial Context for Small Object Detection in UAV-View

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in thefield of small object detection on unmanned aerial vehicles(UAVs).This task is challenging due to variations inUAV flight altitude,differences in object scales,as well as factors like flight speed and motion blur.To enhancethe detection efficacy of small targets in drone aerial imagery,we propose an enhanced You Only Look Onceversion 7(YOLOv7)algorithm based on multi-scale spatial context.We build the MSC-YOLO model,whichincorporates an additional prediction head,denoted as P2,to improve adaptability for small objects.We replaceconventional downsampling with a Spatial-to-Depth Convolutional Combination(CSPDC)module to mitigatethe loss of intricate feature details related to small objects.Furthermore,we propose a Spatial Context Pyramidwith Multi-Scale Attention(SCPMA)module,which captures spatial and channel-dependent features of smalltargets acrossmultiple scales.This module enhances the perception of spatial contextual features and the utilizationof multiscale feature information.On the Visdrone2023 and UAVDT datasets,MSC-YOLO achieves remarkableresults,outperforming the baseline method YOLOv7 by 3.0%in terms ofmean average precision(mAP).The MSCYOLOalgorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets inUAV aerial photography,providing strong support for practical applications.

Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

Fundamental Theories of Spatial Similarity Relations in Multi-scale Map Spaces

Similarity relation is one of the spatial relations in the community of geographic information science and cartography.It is widely used in the retrieval of spatial databases, the recognition of spatial objects from images, and the description of spatial features on maps.However, little achievements have been made for it by far.In this paper, spatial similarity relation was put forward with the introduction of automated map generalization in the construction of multi-scale map databases;then the definition of spatial similarity relations was presented based on set theory, the concept of spatial similarity degree was given, and the characteristics of spatial similarity were discussed in detail, in-cluding reflexivity, symmetry, non-transitivity, self-similarity in multi-scale spaces, and scale-dependence.Finally a classification system for spatial similarity relations in multi-scale map spaces was addressed.This research may be useful to automated map generalization, spatial similarity retrieval and spatial reasoning.

DCAD:a Dual Clustering Algorithm for Distributed Spatial Databases

Spatial objects have two types of attributes： geometrical attributes and non-geometrical attributes, which belong to two different attribute domains （geometrical and non-geometrical domains）. Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering： local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted clustering is obtained based on those features fective and efficient. Second, local features from each site are sent to a central site where global Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.

Spatial Database Management System of China Geological Survey Extent

The spatial database management system of China geological survey extent is a social service system. Its aim is to help the government and the whole social public to expediently use the spatial database, such as querying, indexing, mapping and product outputting. The management system has been developed based on MAPGIS6.x SDK and Visual C++, considering the spatial database contents and structure and the requirements of users. This paper introduces the software structure, the data flow chart and some key techniques of software development.

A CADASTRAL SPATIAL DATA STORAGE STRUCTURE BASED ON RELATIONAL DATABASE

This paper presents a cadastral spatial data storage structure based on relational database,the method and the procedure to realize it.The paper consists of three parts.In the first part,some existing problems in some developed cadastral management systems are discussed.These problems are the following four.1) The security of cadastral spatial data is difficult to be assured.2) It is difficult to varify cadastral data and the integrality of cadastral data is difficult to be kept.3) To transmit and share cadastral data is difficult.4) The efficiency of data access is low.In the second part,the feasibility of using relational database to store spatial data is analyzed and a new cadastral spatial data storage structure is presented.At the same time,the related table structures and field descriptions are given,and then the merits and demerits of this storage structure are analyzed in detail.In the last part,through a real example,the detailed methods to make the new storage structure a reality are given.Moreover,some involving key techniques of the new storage structure are discussed.These techniques are:1) the application of database transaction,2) the application of database trigger,3) and the application of secure recovery of database.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Unified Platform About Information Issuing,Integration and Management of Meta-Database and Spatial-Database Based on Geology Metadata Standard

With the deepening informationization of Resources & Environment Remote Sensing geological survey conducted,some potential problems and deficiency are:(1) shortage of unified-planed running environment;(2) inconsistent methods of data integration;and(3) disadvantages of different performing ways of data integration.This paper solves the above problems through overall planning and design,constructs unified running environment, consistent methods of data integration and system structure in order to advance the informationization

Federated Spatial Databases and Interoperability

It is a period of information explosion. Especially for spatial information science, information can be acquired through many ways, such as man made planet, aeroplane, laser, digital photogrammetry and so on. Spatial data sources are usually distributed and heterogeneous. Federated database is the best resolution for the share and interoperation of spatial database. In this paper, the concepts of federated database and interoperability are introduced. Three heterogeneous kinds of spatial data, vector, image and DEM are used to create integrated database. A data model of federated spatial databases is given.

Processing Constrained K Closest Pairs Query in Spatial Databases

In this paper, constrained K closest pairs query is introduced, wbich retrieves the K closest pairs satisfying the given spatial constraint from two datasets. For data sets indexed by R trees in spatial databases, three algorithms are presented for answering this kind of query. Among of them, two-phase Range＋Join and Join＋Range algorithms adopt the strategy that changes the execution order of range and closest pairs queries, and constrained heap-based algorithm utilizes extended distance functions to prune search space and minimize the pruning distance. Experimental results show that constrained heap-base algorithm has better applicability and performance than two-phase algorithms.

Spatial Database of Geological Survey Extent in 20th Century in China

During the whole 20th century in China, especially the latest 50 years, we have gotten much geological information about geological mapping, geophysics, geochemistry, mineral exploration, remote sensing, environmental geology, hydrogeology, engineering geology and oceanic geology etc. by our geologists and explorers. All the information has been accumulated and can be used as a decision-making foundation for the future plan of geological survey. The spatial database of geological survey extents has been established by using computer technology. The database contained all kinds of exploration sections and collected about 160 000 records in this database. This paper introduces the data construction, contents and applying system of this database, and trys to let people know what kinds of geological survey were finished, when the exploration were carried out, and how and where you can get this information.

THE INTEGRATED SPATIAL DATABASES OF GEOSTAR

GeoStar is the registered trademark of GIS software made by WTUSM in China.By means of the GeoStar,multi_scale images,DEMs,graphics and attributes integrated in very large seamless databases can be created,and the multi_dimensional dynamic visualization and information extraction are also available.This paper describes the fundamental characteristics of such huge integrated databases,for instance,the data models,database structures and the spatial index strategies.At last,the typical applications of GeoStar for a few pilot projects like the Shanghai CyberCity and the Guangdong provincial spatial data infrastructure (SDI) are illustrated and several concluding remarks are stressed.

Concepts and Applications of Spatial Database for Geo-Archives Catalog

The searching method of spatial information on traditional geo-archives catalog database(TGCD) is based on the text,and the result of retrieval can be only from the text of fields of relational database.The information queried must be input into the relational database as a text form in advance,otherwise,the visitors would not get any result from it.So。

Topo semantic constraint-violations in spatial database

Aiming at the problem that current geographical information systems(GIS)usually does not maintain semantic and user-defined constraints out of three consistency-constrains(third refers to topology constraint),this research focuses on building an efficient spatial data management system using two constraint violation detection methods.An algorithm for constraint violation detection has been derived to maintain the error-free up-to-date spatial database.Results indicate that the developed constraint violation detection(CVD)system is more efficient compared with conventional systems.

Construction of Spatial Database of Tourism Resources in the Lijiang River Basin Based on MapGIS

GIS and GPS technology was applied to construct spatial database of ecotourism resources in the Lijiang River Basin,the process of constructing database was elaborated,data collection and processing as well as specific schemes of building graphic database and attribute database were introduced,in order to provide data support for optimizing the development spaces of regional tourism resources,regulating development activities,and realizing sustainable development of ecology,environment,tourism economy and society.In addition,this paper also offered data support for eco-environment monitoring,building tourism resource management information system and "Digital Lijiang".

Compressive imaging based on multi-scale modulation and reconstruction in spatial frequency domain

Imaging quality is a critical component of compressive imaging in real applications. In this study, we propose a compressive imaging method based on multi-scale modulation and reconstruction in the spatial frequency domain. Theoretical analysis and simulation show the relation between the measurement matrix resolution and compressive sensing(CS)imaging quality. The matrix design is improved to provide multi-scale modulations, followed by individual reconstruction of images of different spatial frequencies. Compared with traditional single-scale CS imaging, the multi-scale method provides high quality imaging in both high and low frequencies, and effectively decreases the overall reconstruction error.Experimental results confirm the feasibility of this technique, especially at low sampling rate. The method may thus be helpful in promoting the implementation of compressive imaging in real applications.

Multi-scale spatial relationships between soil total nitrogen and influencing factors in a basin landscape based on multivariate empirical mode decomposition

The relationships between soil total nitrogen(STN)and influencing factors are scale-dependent.The objective of this study was to identify the multi-scale spatial relationships of STN with selected environmental factors(elevation,slope and topographic wetness index),intrinsic soil factors(soil bulk density,sand content,silt content,and clay content)and combined environmental factors(including the first two principal components(PC1 and PC2)of the Vis-NIR soil spectra)along three sampling transects located at the upstream,midstream and downstream of Taiyuan Basin on the Chinese Loess Plateau.We separated the multivariate data series of STN and influencing factors at each transect into six intrinsic mode functions(IMFs)and one residue by multivariate empirical mode decomposition(MEMD).Meanwhile,we obtained the predicted equations of STN based on MEMD by stepwise multiple linear regression(SMLR).The results indicated that the dominant scales of explained variance in STN were at scale 995 m for transect 1,at scales 956 and 8852 m for transect 2,and at scales 972,5716 and 12,317 m for transect 3.Multi-scale correlation coefficients between STN and influencing factors were less significant in transect 3 than in transects 1 and 2.The goodness of fit root mean square error(RMSE),normalized root mean square error(NRMSE),and coefficient of determination(R2)indicated that the prediction of STN at the sampling scale by summing all of the predicted IMFs and residue was more accurate than that by SMLR directly.Therefore,the multi-scale method of MEMD has a good potential in characterizing the multi-scale spatial relationships between STN and influencing factors at the basin landscape scale.

A recovery method using recently updated record information in shared-nothing spatial database cluster

A shared nothing spatial database cluster is system that provides continuous service even if some system failure happens in any node. So, an efficient recovery of system failure is very important. Generally, the existing method recovers the failed node by using both cluster log and local log. This method, however, cause several problems that increase communication cost and size of cluster log. This paper proposes novel recovery method using recently updated record information in shared nothing spatial database cluster. The proposed technique utilizes update information of records and pointers of actual data. This makes a reduction of log size and communication cost. Consequently, this reduces recovery time of failed node due to less processing of update operations.

An Introduction to the Latest-generation Spatial Database of Active Tectonics of China

Based on ArcGIS and MapInfo software, we digitized the active tectonics map (1:4,000,000) of China, which was compiled and revised by academician Deng Qidong, and built the spatial database of active tectonics of China. The database integrates rich active tectonic data, such as a catalogue of earthquakes with magnitude above 6.0, active faults, Quaternary basins, active folds and their associated attribute parameters, and implements scientific and effective management to this data. At the same time, the spatial database joins the spatial map data and the associated attribute data together, which implements the data query between spatial properties and attribute parameters and also makes it possible to perform spatial analysis with different data layers. These provide much convenience for earthquake study and allows engineering construction institutions to use this data in practical applications.

A solution for digital image management in spatial databases

Digital Orthographic Map (DOM) can be used in various applications because it contains both image features and terrain information. Spatial database management systems aim at the effective and efficient management of data related to a space, engineering design and so on. Thereby spatial database provides an efficient solution for managing DOM. According to large amounts of the DOM data in storage, a data compression based on wavelet is introduced into the storage. Another strategy to solve this problem is to decompose the raw image into tiles and store the tiles individually as separate tuples. The metadata of DOM can be used to organize and manage spatial information, especially for spatial data sharing and fast locating. A tool for browsing, zooming and querying the DOM data is also designed. We implemented these ideas in SISP(Spatial Information Sharing System) and applied the subsystem into the DOM management of Beijing City, which is an component of the Beijing Spatial Information Infrastructure.