System and Method for Determining Illegal Occupancy Facilities in Small Streams Based on Legal Rules

Abstract

The present invention relates to technology for supporting small stream management and administrative decisions. In a system for determining whether facilities within a small stream are illegally occupied using multiple spatial data, The system is characterized by comprising: a data collection unit that collects multiple image data, spatial data, user report data, and administrative processing history data; a reliability evaluation unit that calculates a reliability score for each of the multiple data using at least one of resolution, shooting time, distortion correction value, positional accuracy, spatial overlap of reports, and temporal coincidence; a data fusion unit that generates facility candidate objects by weighted fusion of the multiple data based on the reliability score; a regulatory area generation unit that generates a regulatory area for facility judgment according to a river centerline or legal standards; a spatial analysis unit that analyzes the spatial relationship between the facility candidate objects and the regulatory area; a legal judgment engine that determines the state of the facility candidate objects as normal, suspicious, or illegal based on the spatial analysis results and the user report data; a state transition management unit that updates and manages the state over time; a result generation unit that generates the judgment results in the form of map-based objects or a list of administrative action targets; and an output unit that provides the results as a map-based visualization screen, a dashboard, or data linked to an administrative processing system.

Description

Technology Field

[0001] The present invention relates to a technology for supporting small stream management and administrative decisions, and,

[0002] More specifically, the invention relates to a legal rule-based system and method for determining illegal occupation of small streams by comprehensively utilizing drone imagery, aerial photographs, satellite imagery, cadastral maps, stream boundary data, land use information, user report data, and administrative processing history data to automatically determine whether facilities within or adjacent to a small stream are illegally occupied and to continuously manage their status over time. Background Technology

[0004] Small streams are public water resource spaces closely linked to the lives of local residents, and are important management targets for disaster prevention, maintaining water flow functions, preserving ecosystems, and ensuring public safety.

[0005] If unauthorized installation of facilities, illegal occupation, stockpiling of materials, construction of arbitrary structures, retention of temporary structures, or continuous land use occurs within a small stream area or an adjacent area requiring management, it may result in a reduction of the water flow cross-section, obstruction of water flow, increased risk of flooding, deterioration of the stability of embankments or slopes, and impediment to public use.

[0006] The review materials provided by the user also explain that the unauthorized installation of facilities, illegal occupation, illegal stockpiling, or arbitrary structures within small streams leads to increased disaster risk and impairs public safety.

[0007] However, conventional methods for managing illegal occupation of small streams have limitations as they mostly rely on on-site inspections, civil complaints, visual inspection by officials in charge, or individual GIS inquiries.

[0008] In other words, there are problems such as the fact that on-site investigations require a significant amount of manpower and time, it is difficult to make a comprehensive judgment due to the dispersion of multiple heterogeneous data, and the determination of illegality heavily depends on the experience and subjectivity of the person in charge.

[0009] The provided data also notes that conventional methods rely on on-site inspections, civil complaints, or manual analysis by administrative agencies, which consumes a significant amount of time and manpower, makes it difficult to utilize multiple data sets in an integrated manner, and has limitations as the determination of illegality depends on the experience and subjectivity of the person in charge.

[0010] In particular, whether a small stream is illegally occupied cannot be determined solely on the grounds that “facilities are visible.”

[0011] Even for the same facility, the judgment result may vary depending on the time of filming, video resolution, positional accuracy, time of reporting, accuracy of the reported location, consistency of cadastral information, location of the river centerline, separation distance standards under relevant laws, occupied area or occupancy rate, history of past legalization or administrative processing, etc.

[0012] Therefore, to automate legal judgments, it is necessary to go beyond simple object detection and integrate data reliability assessment, automatic generation of regulatory domains, quantitative spatial analysis, application of legal rule engines, and continuous management of judgment status.

[0013] Conventional GIS systems or civil complaint management systems often remain at the level of map viewing, object location verification, civil complaint reception, simple buffer analysis, or one-time classification.

[0014] On the other hand, the data provided by the user compares the existing system with the present invention and specifies that the present invention possesses distinct features such as “reliability-based weighted fusion,” “automatic generation of regulatory areas reflecting legal standards,” “automatic judgment based on a legal judgment engine,” “state transition-based continuous management,” and “ability to link with administrative processing systems.”

[0015] This directionality becomes the key differentiating point of the present invention.

[0016] Therefore, the technical significance of the present invention lies in establishing a legal rule-based automatic judgment system to increase the possibility of determining illegal administrative occupation, rather than simply detecting facilities, and providing a system combined with a continuously manageable state transition structure. Prior art literature

[0018] Patent Document 1: Korean Patent Publication No. 10-2026-0021258 Patent Document 2: Korean Patent Publication No. 10-2025-0011448 The problem to be solved

[0019] The objective of the present invention is to provide a system and method for automatically determining whether facilities within a small stream or a regulated area are illegally occupied by utilizing a combination of image data, spatial data, report data, and administrative processing history data.

[0020] Another objective of the present invention is to evaluate the reliability of data from different sources based on criteria such as resolution, time of capture, location accuracy, report overlap, and time consistency, and to generate facility candidate objects through weighted fusion reflecting this.

[0021] Another objective of the present invention is to automatically generate a regulated area by reflecting the river centerline, river boundary, or legal distance criteria, and to set different variable buffers for left and right sections or for legal conditions.

[0022] Another objective of the present invention is to quantitatively calculate the intersection status, separation distance, occupied area, occupancy rate, and cumulative reporting rate between a candidate facility object and a regulated area, and to input these values ​​into legal judgment rules to automatically determine whether the state is normal, suspicious, or illegal.

[0023] Another objective of the present invention is to not end the above-mentioned judgment results as a one-time event, but to manage them over time in normal, suspicious, illegal, under action, and legalized states, and to link them with the history of administrative actions.

[0024] Another objective of the present invention is to implement an operational system usable in actual public administration by providing map-based visualization, a list of targets for administrative measures, a dashboard, and data linked to external administrative systems. means of solving the problem

[0026] To achieve the above objective, a legal rule-based small stream illegal occupation facility determination system according to one embodiment of the present invention is,

[0027] A data collection unit that collects multiple image data, spatial data, user report data, and administrative processing history data; a reliability evaluation unit that calculates a reliability score for each of the multiple data using at least one of resolution, shooting time, distortion correction value, positional accuracy, spatial overlap of reports, and temporal coincidence; a data fusion unit that generates facility candidate objects by weighted fusion of the multiple data based on the reliability score; a regulatory area generation unit that automatically generates a regulatory area for facility determination based on a river centerline, river boundary, or legal distance criteria; a spatial analysis unit that calculates at least one of intersection, distance, occupied area, and occupancy rate by analyzing the spatial relationship between the facility candidate objects and the regulatory area; a legal judgment engine that determines the status of the facility candidate objects as normal, suspicious, or illegal based on the spatial analysis results and the user report data; a result generation unit that generates the judgment results in the form of map-based objects, lists of administrative action targets, or linked data; and an output unit that provides the results as map-based visualization screens, dashboards, or data linked to administrative processing systems. and may include a state transition management unit that updates and manages the state of the above facility candidate object to at least one of normal, suspicious, illegal, under action, and legalized states over time.

[0028] In addition, according to the present invention, the regulation area generating unit can generate a regulation area using a variable buffer method that applies the same or different legal distance standards to the left and right sides of the river centerline.

[0029] In addition, according to the present invention, the legal judgment engine can make a judgment by combining a plurality of judgment conditions using a combination of AND, OR, priority rules, or weighting rules.

[0030] In addition, according to the present invention, the state transition management unit may update the state by using at least one of the following as a transition condition: occurrence of a new report, repeated report, exceedance of a standard, results of an on-site inspection, results of administrative measures, submission of legalization documents, or the passage of time.

[0031] In addition, according to the present invention, the data fusion unit can calculate the probability of facility existence, location reliability, or object reliability grade based on the reliability scores of multiple data corresponding to the same facility candidate. Effects of the invention

[0033] According to the present invention, going beyond the level of simple map inquiry or simple object detection, facility candidates are generated by fusing multiple heterogeneous data based on reliability, thereby increasing the reliability of basic data for determining illegal occupation.

[0034] In addition, according to the present invention, since a regulated area can be automatically generated based on the river centerline and legal standards, consistent legal judgment criteria can be applied without arbitrary interpretation by the person in charge or manual buffer generation.

[0035] In addition, according to the present invention, since automatic determination can be performed through a legal judgment engine that combines information such as whether there is an intersection, separation distance, occupied area, occupancy rate, and reporting information, consistency and explainability of administrative judgment can be simultaneously secured.

[0036] In addition, according to the present invention, since the judgment results are continuously managed in a state transition manner, it is possible to manage, over the long term, whether administrative measures have been implemented, whether violations have been repeated, and whether legalization has been established, rather than just a one-time detection.

[0037] In addition, according to the present invention, results can be output in the form of map-based visualization, a dashboard, a list of administrative measures, and linkage with external systems, so they can be immediately utilized in actual administrative processing tasks.

[0038] Meanwhile, this invention was carried out with the support of the National Institute of Disaster and Safety Research under the Ministry of the Interior and Safety ("Development of Disaster Analysis Technology Utilizing Satellite Imagery Data", "NDMI-Major-2026-03-03"). We express our gratitude for this. Brief explanation of the drawing

[0039] FIG. 1 is an overall configuration diagram of a legal rule-based small stream illegal occupation facility determination system according to one embodiment of the present invention. FIG. 2 is a reliability evaluation and data fusion structure diagram according to one embodiment of the present invention. FIG. 3 is a conceptual diagram of a regulatory area generation according to one embodiment of the present invention. FIG. 4 is a flowchart of spatial analysis and legal judgment according to one embodiment of the present invention. FIG. 5 is a state transition model diagram according to one embodiment of the present invention. FIG. 6 is a flowchart of an example of reliability score calculation and weighted fusion according to one embodiment of the present invention. FIG. 7 is an example diagram of a set of legal judgment rules according to one embodiment of the present invention. FIG. 8 is a diagram of the administrative processing linkage and status update procedure according to one embodiment of the present invention. Specific details for implementing the invention

[0040] Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the accompanying drawings. However, it should be understood that the scope of the present invention is not limited thereto.

[0041] In this specification, the embodiments are provided to ensure that the disclosure of the invention is complete and to fully inform those skilled in the art of the scope of the invention, and the scope of the invention is defined only by the claims. Accordingly, in some embodiments, well-known components, well-known operations, and well-known techniques are not specifically described to avoid the invention being interpreted ambiguously.

[0042] The terms used herein are for describing embodiments and are by no means intended to limit the invention. In this specification, the singular form includes the plural form unless specifically stated otherwise in the text. Additionally, components and operations referred to as "comprising (or comprising)" do not exclude the presence or addition of one or more other components and operations.

[0043] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the present invention is not limited to the following embodiments, and various modifications are possible within the scope of the technical spirit of the present invention.

[0045] 1. Basic Concepts

[0046] The core of the present invention lies not merely in indicating the location of a facility, but in (1) quantifying the reliability of multiple data, (2) generating facility candidate objects based on reliability, (3) automatically generating a regulated area according to legal standards, (4) quantitatively analyzing the spatial relationship between the facility candidate objects and the regulated area, (5) automatically determining whether it is normal, suspicious, or illegal through a legal judgment engine, and (6) continuously managing the judgment result in a state transition manner.

[0047] This structure aligns with the core composition of the data provided by the user.

[0048] The technical features of the present invention are broadly divided into five categories.

[0049] First is reliability-based multi-data fusion.

[0050] Since video, spatial, reporting, and administrative history data have different qualities and accuracy, they are not used with equal weight but are fused after undergoing a quantitative reliability evaluation.

[0051] Second, it is the automatic generation of regulatory areas reflecting legal standards.

[0052] Automatically generates a regulated area considering the river centerline, river boundary, legal distance, and left and right conditions.

[0053] Third, it is a legal judgment based on quantitative spatial analysis.

[0054] The location of facilities, whether they intersect, separation distance, occupied area, and occupancy rate are quantified and entered into the legal judgment rules.

[0055] Fourth, it is an explainable legal judgment engine.

[0056] Since the judgment conditions are composed of AND, OR, or weighting rules, the basis for judgment can be presented.

[0057] Fifth, it is state transition-based continuous management.

[0058] The continuity and history of administrative operations are ensured by managing judgment results as normal, suspicious, illegal, under action, and legalized states.

[0060] 2. Data Collection Department

[0061] The data collection unit (100) collects image data (110), spatial data (120), report data (130), and administrative processing history data (140).

[0062] The image data (110) may include at least one of drone images, aerial photographs, and satellite images.

[0063] The draft provided by the user also states that the image data includes at least one of drone footage, aerial photographs, and satellite images.

[0064] Video data is used to identify the outer contours, locations, changes over time, and occupancy patterns of facilities.

[0065] Spatial data (120) may include river boundary data, cadastral map, land use information, river centerline information, administrative district boundary, digital topographic map, or legal district data.

[0066] The provided data explains that the spatial data includes river boundary data, cadastral maps, and land use information.

[0067] Report data (130) may include resident complaints, mobile reports, on-site reports, reports with attached photos, reports with specified locations, and history of repeated reports.

[0068] Administrative processing history data (140) may include past field inspection results, warnings or corrective orders, requests for removal, imposition of fines, applications for legalization, whether permission was granted, whether measures were completed, whether compliance was denied, etc.

[0069] The data collection unit (100) can collect these data on a regular or irregular basis and store them together with time metadata to be used for subsequent decision-making.

[0071] 3. Reliability Evaluation Department

[0072] The reliability evaluation unit (200) quantitatively evaluates the quality of each collected data.

[0073] The resolution evaluation module (210) evaluates the spatial resolution or object identification level of the image data.

[0074] For example, a higher score can be assigned as the unit pixel size is small and the outline of the facility is clearly identifiable.

[0075] The shooting time evaluation module (220) evaluates the freshness of the data or its proximity to a reference time. For example, a high score may be assigned to a recently captured video or a video close to the time of reporting.

[0076] The position accuracy evaluation module (230) evaluates the coordinate error, alignment error, sensor error, or accuracy of the reported position.

[0077] The report overlap evaluation module (240) calculates the reliability of reports by analyzing the degree to which multiple reports accumulate in the same or adjacent area.

[0078] The time consistency evaluation module (250) evaluates how much the video time, the report time, and the administrative history time match each other.

[0079] The reliability score calculation unit (260) can calculate the reliability score Ri for each data using the above indicators.

[0080] For example, the confidence score for data i can be defined as follows.

[0081]

[0082] am.

[0083] For example, while drone footage has high resolution, the total score may decrease if the recording date is old. Conversely, although resident reports do not have the concept of resolution like video, the reliability score can increase if there are repeated reports and a high degree of time consistency.

[0084] In this way, the reliability evaluation unit (200) does not mechanically process the data identically, but provides a quantitative basis for subsequent fusion.

[0085] This directly corresponds to the “trust-based weighted convergence” structure emphasized in the provided materials.

[0087] 4. Data Fusion Department

[0088] The data fusion unit (300) generates facility candidate objects based on the reliability evaluation results.

[0089] The weight calculation unit (310) calculates the fusion weight Wi using the reliability score Ri for each data. For example,

[0090]

[0091] It can be defined as.

[0092] The facility candidate object generation unit (320) clusters facility candidates of the same or adjacent locations extracted from different data sources and generates a final candidate object by reflecting weights.

[0093] For example, if an object is detected in drone footage, overlaps with a specific parcel on a cadastral map, has repeated reports at the same location, and has a history of past inspections, the candidate confidence of that object increases.

[0094] Conversely, if only a single report exists, objects in the image are not identified, and the positional error is large, the reliability of the candidate objects may be low.

[0095] The object confidence level calculation unit (330) can assign a confidence level such as “high / medium / low” to the final candidate object.

[0096] This confidence level can be used as an auxiliary judgment factor in the legal judgment engine described later.

[0098] 5. Regulatory Area Creation Section

[0099] The regulation area generation unit (400) automatically generates a regulation area based on the river centerline, river boundary, and legal standards.

[0100] The river centerline generation module (410) extracts a river centerline from spatial data or aligns existing centerline data.

[0101] The legal distance standard application module (420) sets distance values ​​according to small stream-related laws, ordinances, administrative guidelines, or separation standards by type of facility.

[0102] The left and right variable buffer generation module (430) can apply the same or different buffer widths to the left and right sides of the centerline.

[0103] This is consistent with the “variable buffer method applying legal distance standards to the left and right sides of the river centerline” in the provided data.

[0104] In a preferred embodiment, the regulation area generating part (400) may have the following modified structure.

[0105] Example 1: Buffers of equal left and right distances relative to the centerline

[0106] Example 2: Different distance buffers depending on the terrain or legal conditions on the left and right

[0107] Example 3: Regulatory area where buffer width varies depending on facility type

[0108] Example 4: Regulated areas where separate rules apply in special sections such as bridges, embankments, and water passage sections

[0109] In addition, the regulation area generation unit (400) may include a regulation area correction function based on land use information or cadastral boundaries, in addition to simple buffer generation.

[0111] 6. Spatial Analysis Department

[0112] The spatial analysis unit (500) analyzes the spatial relationship between the facility candidate object and the regulation area.

[0113] The coordinate extraction module (510) extracts the center point, outer polygon, or boundary line of the facility candidate object.

[0114] The cross-analysis module (520) analyzes whether a facility candidate object is included within the regulated area, intersects with the boundary, or is located outside.

[0115] The separation distance calculation module (530) calculates the shortest distance between a candidate facility object and the boundary of the regulated area or the centerline of the river.

[0116] The occupied area calculation module (540) calculates the area where a facility candidate object overlaps with the regulated area.

[0117] The occupancy rate calculation module (550) calculates the ratio of the occupied area within the regulated area to the total area of ​​the facility or the occupancy rate to the area of ​​the corresponding river section.

[0118] This analysis structure directly corresponds to the “calculation of intersection, distance, occupied area, and occupancy rate between candidate facility objects and regulated areas” in the provided data.

[0119] For example, there may be different risk levels and judgment results depending on whether facility A is completely contained within the regulated area and has an occupancy rate of 80% or more, or facility B is located outside the regulated area but is within 0.5m of the boundary.

[0121] 7. Legal Judgment Engine

[0122] The legal judgment engine (600) determines the condition of the facility as normal, suspicious, or illegal based on the spatial analysis results and reported data.

[0123] The rule storage unit (610) stores legal judgment rules. These rules may take the form of AND, OR, priority rules, or weighting rules. The provided data also specifies that multiple judgment conditions can be combined into a combination of AND, OR, or weighting rules.

[0124] The condition combination judgment module (620) combines the input rules to perform a final judgment.

[0125] The status determination module (630) determines the final result as normal, suspicious, or illegal.

[0126] The exemplary rules are as follows.

[0127] (1) Example of normal judgment

[0128] Located outside the regulatory area

[0129] The cumulative reporting rate is low and

[0130] If there is no history of past violations → Normal

[0131] (2) Example of doubt judgment

[0132] within a certain distance of the boundary of the regulatory area and

[0133] If there is one or more reports or

[0134] The object has a high reliability rating.

[0135] If the occupancy rate is below a predetermined threshold → Suspicion

[0136] (3) Examples of illegal judgments

[0137] Intersects with regulatory areas

[0138] The occupied area or occupancy rate is above a threshold and

[0139] If violations exist in the cumulative number of reports or administrative history → Illegal

[0140] Also, as an example of a weighting rule, the final judgment score J

[0141]

[0142] It can be defined as.

[0143] Here

[0144]

[0145] am.

[0146] For example,

[0147]

[0148] It can be classified as.

[0149] As such, the judgment engine of the present invention does not rely solely on the criterion of "inside / outside the regulated area," but combines multiple conditions in an explainable manner.

[0151] 8. Result Generation Unit and Output Unit

[0152] The result generation unit (700) generates the judgment result in a form that can be used administratively.

[0153] The map object creation module (710) can display facility objects as a map layer and assign different colors or symbols depending on the state.

[0154] The administrative action target list generation module (720) can be configured to output a list of illegal or suspected facilities and include location, status, occupancy rate, reporting history, and the time of the most recent determination.

[0155] The output unit (800) may include a map visualization module (810), a dashboard output module (820), and an external system linkage module (830).

[0156] The map visualization module (810) can display the regulated area, facility candidate objects, judgment status, and report density on the map.

[0157] The dashboard output module (820) can provide the number of suspected illegal cases, number of repeated violations, number of cases awaiting action, number of cases in progress of legalization, etc., by region in the form of a chart or table.

[0158] The external system linkage module (830) can link results to an administrative processing system, a civil complaint management system, or an inspection management system.

[0159] The provided data also presents the possibility of linking with administrative processing systems as a differentiating factor.

[0161] 9. State Transition Management Department

[0162] The state transition management unit (900) manages the state of the facility candidate object over time.

[0163] The state storage module (910) stores the current state and past state history of each facility object.

[0164] The transition condition evaluation module (920) performs a state transition according to a specific event or condition.

[0165] The history tracking module (930) tracks whether the same facility has repeated violations, whether there is a delay in taking action, or whether it has been legalized over the long term.

[0166] The provided data explains that normal, suspicious, illegal, in action, and legalized statuses are managed over time, and presents “report occurrence / analysis change,” “repeated reporting / exceeding of standards,” and “administrative action / legalization” as transition factors in the state transition model.

[0167] An exemplary state transition rule is as follows.

[0168] Normal → Suspicious: New report occurrence, change in analysis results, increased boundary proximity

[0169] Suspicious → Illegal: Repeated reports, exceeding occupancy limits, violations found during on-site verification

[0170] Illegal → Action in Progress: Commencement of administrative measures, notification of corrective order

[0171] In Action → Legalization: Legalization approval or permission completed

[0172] In Action → Continued Illegal: Failure to take action within the deadline or repeated violations

[0173] Legalization → Normal: Confirmation of final legal status

[0174] This structure significantly enhances the continuity of administrative tasks and the effectiveness of follow-up management.

[0176] 10. Example 1: Generation of Candidate Objects Based on Multiple Data Fusion

[0177] In this embodiment, drone footage, cadastral maps, resident reports, and past administrative history are collected for the same small stream section.

[0178] Drone footage has a high reliability score because it has high resolution, was filmed recently, and matches the reported location well.

[0179] On the other hand, older satellite imagery has a low weight because, although the locations are roughly accurate, it lacks up-to-dateness.

[0180] The data fusion unit (300) combines these data to create a final facility candidate object.

[0182] 11. Example 2: Automatic Generation of Regulatory Areas and Spatial Analysis

[0183] In this embodiment, a regulated area is created by applying legal distances d1 and d2 to the left and right sides of the small stream centerline.

[0184] Facility A is located inside the left regulated area, and Facility B exists outside the right regulated area.

[0185] The spatial analysis unit (500) calculates that the occupied area and occupancy rate of facility A are high, and the legal judgment engine (600) determines this to be illegal.

[0186] On the other hand, facility B can be determined to be normal. This embodiment is consistent with the structures of Figures 3 and 4 of the provided materials.

[0188] 12. Example 3: State Transition-Based Administrative Management

[0189] In this embodiment, a facility object is initially adjacent to a regulated area, but is classified as "suspicious" because it is unclear whether it intersects.

[0190] Subsequently, as repeated reports accumulate and the share increases based on the analysis of new videos, the status transitions to “Illegal.” When administrative measures are initiated, the status changes to “Action in Progress,” and then transitions to “Legalized” upon the submission and approval of legalization documents.

[0191] This embodiment serves as an example of actual operation of the state transition management unit (900).

[0193] 13. Deformation Examples and Reinforcement Examples

[0194] The present invention is not limited to the above embodiments and can be modified as follows.

[0195] Additional use of aerial LiDAR or orthophotos instead of drone footage

[0196] Creation of multiple regulatory areas by section instead of a single regulatory area

[0197] Added AI-based assisted recommendation feature to judgment rules

[0198] Automatic assignment of priority inspection order for facilities with repeated violations

[0199] Added seasonal shooting cycle or pre- and post-disaster comparison features

[0200] Different judgment rules apply based on facility types, such as illegal stockpiling, temporary warehouses, containers, fences, and temporary structures.

[0202] Although the technical concept of the present invention has been specifically described in preferred embodiments, it should be noted that the aforementioned embodiments are for illustrative purposes only and are not intended to be limiting. It is obvious to those skilled in the art that various modifications and variations are possible within the scope of the technical concept of the present invention, and therefore, it is natural that such modifications and variations fall within the scope of the appended claims. Explanation of the symbols

[0204] 100: Data collection unit 110: Image data 120: Spatial data 130: Report data 140: Administrative processing history data 200: Reliability evaluation section 210: Resolution evaluation module 220: Shooting time evaluation module 230: Location Accuracy Evaluation Module 240: Report Overlap Evaluation Module 250: Time Agreement Evaluation Module 260: Reliability Score Calculator 300: Data Fusion Unit 310: Weight Calculation Unit 320: Facility Candidate Object Generation Unit 330: Object Trust Level Calculation Unit 400: Regulatory Area Generation Unit 410: River Centerline Generation Module 420: Legal distance standard application module 430: Left / Right variable buffer generation module 500: Spatial Analysis Unit 510: Coordinate Extraction Module 520: Cross-analysis module 530: Separation distance calculation module 540: Occupancy Area Calculation Module 550: Occupancy Rate Calculation Module 600: Legal Judgment Engine 610: Rule Storage 620: Condition combination judgment module 630: State judgment module 700: Result Generation Unit 710: Map Object Creation Module 720: Administrative Action Target List Generation Module 800: Output Section 810: Map Visualization Module 820: Dashboard Output Module 830: External System Linkage Module 900: State Transition Management Unit 910: State storage module 920: Transition condition evaluation module 930: History Tracking Module

Claims

Claim 1 A system for determining whether facilities within a small stream are illegally occupied using multiple spatial data comprises: a data collection unit that collects multiple image data, spatial data, user report data, and administrative processing history data; a reliability evaluation unit that calculates a reliability score for each of the multiple data using at least one of resolution, shooting time, distortion correction value, positional accuracy, spatial overlap of reports, and temporal coincidence; a data fusion unit that generates facility candidate objects by weighted fusion of the multiple data based on the reliability score; a regulatory area generation unit that generates a regulatory area for facility determination according to the stream centerline or legal standards; a spatial analysis unit that analyzes the spatial relationship between the facility candidate objects and the regulatory area; a legal judgment engine that determines the state of the facility candidate objects as normal, suspicious, or illegal based on the spatial analysis results and the user report data; a state transition management unit that updates and manages the state over time; and a result generation unit that generates the judgment results in the form of map-based objects or a list of targets for administrative action. A legal rule-based system for determining illegal occupation facilities in small streams, characterized by including an output unit that provides the above results as map-based visualization screens, dashboards, or data linked to administrative processing systems. Claim 2 A legal rule-based system for determining illegal occupation of small streams, characterized in that, in claim 1, the image data includes at least one of drone imagery, aerial photographs, and satellite imagery. Claim 3 A legal rule-based system for determining illegal occupation of small streams, characterized in that, in claim 1, the spatial data includes stream boundary data, cadastral maps, and land use information. Claim 4 A legal rule-based system for determining illegal occupation of small streams, wherein, in claim 1, the reliability evaluation unit calculates a reliability score for each data as a weighted sum of a resolution score, a data time point evaluation score, a location accuracy score, a report overlap score, and a time agreement score, wherein the data time point evaluation score is a score that is calculated higher as the time point of shooting of the image data, the time point of reporting of the user report data, or the time point of processing of the administrative processing history data approaches the reference time. Claim 5 A legal rule-based small stream illegal occupation facility determination system according to claim 1, wherein the data fusion unit clusters facility candidates of the same or adjacent locations extracted from different data sources and generates a final facility candidate object by assigning weights proportional to the reliability score. Claim 6 A legal rule-based small stream illegal occupation facility determination system according to claim 1, wherein the regulation area generating unit generates a regulation area using a variable buffer method applying legal distance standards to the left and right sides of the stream centerline. Claim 7 A legal rule-based system for determining illegal occupation of a small stream, wherein, in claim 1, the regulatory area generating unit applies different distance standards to the left regulatory area and the right regulatory area. Claim 8 A legal rule-based small stream illegal occupation facility determination system according to claim 1, wherein the spatial analysis unit calculates at least one of whether there is an intersection between the facility candidate object and the regulated area, the distance, the occupied area, and the occupancy rate. Claim 9 A legal rule-based small stream illegal occupation facility judgment system according to claim 1, wherein the legal judgment engine performs a judgment by combining a plurality of judgment conditions using AND, OR, or weighted rule combinations. Claim 10 A legal rule-based small stream illegal occupation facility determination system according to claim 9, wherein the legal judgment engine calculates a final judgment score using at least one of whether it intersects with a regulated area, separation distance, occupied area, occupancy rate, candidate object reliability, and report history score. Claim 11 A legal rule-based small stream illegal occupation facility determination system according to claim 1, wherein the state transition management unit manages the state of the facility candidate object as at least one of normal, suspicious, illegal, in action, and legalized. Claim 12 A legal rule-based system for determining illegal occupation of small streams, wherein, in claim 11, the state transition management unit changes the state using at least one of the occurrence of a new report, a repeated report, exceedance of a standard, initiation of administrative measures, approval of legalization, or continued non-action as a transition condition. Claim 13 A legal rule-based system for determining illegal occupation of small streams, characterized in that, in claim 1, the result generating unit generates a list of administrative action targets together with a map-based object, and the list includes at least one of location, status, occupancy rate, reporting history, and the most recent determination time. Claim 14 A legal rule-based small stream illegal occupation facility determination system according to claim 1, wherein the output unit provides the result as at least one of a map-based visualization screen, a dashboard, and data linked to an external administrative system. Claim 15 A method for determining whether a facility within a small stream is illegally occupied using multiple spatial data, comprising: a step of collecting multiple image data, spatial data, user report data, and administrative processing history data; a step of calculating a reliability score for each of the multiple data; a step of generating a facility candidate object by weighted fusing the multiple data based on the reliability score; a step of generating a regulatory area for facility determination based on a stream centerline or legal standards; a step of analyzing the spatial relationship between the facility candidate object and the regulatory area; a step of determining the state of the facility candidate object as normal, suspicious, or illegal based on the spatial analysis result and the user report data; a step of updating and managing the state over time; a step of generating the determination result in the form of a map-based object or a list of administrative action targets; and a step of providing the result as a map-based visualization screen, a dashboard, or data linked to an administrative processing system. Claim 16 A method for determining illegal occupation of a small stream based on legal rules, wherein the step of calculating the reliability score according to claim 15 includes the step of calculating a reliability score for each data using resolution, shooting time, location accuracy, report overlap, and time agreement. Claim 17 A method for determining illegal occupation of a small stream based on legal rules, wherein, in claim 15, the step of creating the regulated area includes the step of creating a variable buffer area by applying legal distance standards to the left and right sides of the stream centerline. Claim 18 A method for determining illegal occupation facilities in a small stream based on legal rules according to claim 15, wherein the determining step comprises a step of calculating a final judgment score by combining at least one of whether there is an intersection, distance, occupied area, occupancy rate, candidate object reliability, and reporting history. Claim 19 A method for determining illegal occupation of a small stream based on legal rules, wherein the step of updating and managing the state according to claim 15 includes the step of updating the state by evaluating transition conditions between normal, suspected, illegal, under action, and legalized states. Claim 20 A computer-readable recording medium having a program recorded thereon for executing the method according to claim 15 on a computer.

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