南浔区私搭乱建遥感监测识别数据

用于实现对南浔区私搭乱建遥感监测中问题点位的高效精准识别，主要识别内容为四边三化中的私搭乱建。算法通过问题点位在影像中的相对位置，自动计算出现问题的实地坐标，便于确定需要进行实地处理的位置。并且自动识别点位问题类型，精准发现建筑物新增与翻建。为后续的人员管理与问题派发提供方便，有利于网格员及时掌握自己管理区域的情况。解决了网格员在传统人工巡查中难以发现问题，容易忽略问题，巡查范围太大，隐蔽地段、偏远地区与恶劣环境中不易巡查等问题。将系统识别出的问题点位派发给网格员，使网格员有依据、有目标的实地确认，极大的提高了发现问题的效率，节约人员时间与人工成本，避免网格员出现无效的巡查。基于无人机航拍采集的私搭乱建遥感影像数据，通过YOLO算法进行实时目标检测。首先将单元神经网络应用于2025年3月的遥感影像，将图像分割成19x19的单元格，每个单元神经网络负责预测K个单元格。预测每个区域的概率，所有单元格上具有最大概率的类被选择并分配给特定的网格单元，生成预测点坐标（x，y），坐标系为CGCS2000，坐标为东经、北纬。
在预测类概率后，进行NMS运算，来消除不必要的锚点。算法识别下一个最高类别概率的边界框，并进行相同的运算过程，直到剩下所有不同的边界框。算法输出所需的要素，并显示各个类的边界框的细节。
抽取部分样本进行识别准确度验证，小于0.6视为识别错误，显示为FALSE；一般样本的识别准确度在0.8至1之间，大于等于0.6视为识别正确，显示为TRUE。通过判断结果正确或错误来纳入或排除数据，将识别正确的点位判定为私搭乱建类别。最后将纳入的点位坐标、问题类型等信息自动上传至私搭乱建智能监管平台，获得南浔区私搭乱建遥感监测识别数据。

This dataset is designed for efficient and accurate recognition of problem locations in remote sensing monitoring of unauthorized construction in Nanxun District, mainly targeting unauthorized construction under the "Four Edges and Three Improvements" initiative. The algorithm automatically calculates the on-site coordinates of problematic areas based on their relative positions in remote sensing images, facilitating the determination of locations requiring on-site remediation. Additionally, it automatically identifies the type of problem at each point, accurately detecting newly added and renovated buildings. This facilitates subsequent personnel management and task assignment, and enables grid inspectors to timely grasp the status of their managed areas. It solves the pain points of traditional manual inspections by grid inspectors, including difficulty in detecting problems, proneness to overlooking issues, overly large inspection scope, and challenges in inspecting hidden, remote areas and harsh environments. By assigning the problem locations identified by the system to grid inspectors, it enables them to conduct on-site verification with clear basis and targets, greatly improving the efficiency of problem detection, saving personnel time and labor costs, and eliminating ineffective inspections by grid inspectors.

The dataset is built upon remote sensing image data of unauthorized construction collected via unmanned aerial vehicle (UAV) aerial photography, and real-time object detection is performed using the YOLO algorithm. First, the grid-based neural network is applied to the remote sensing images from March 2025, which divides the images into 19×19 grid cells. Each grid cell is responsible for predicting K bounding boxes. It predicts the probability of each region, selects the class with the highest probability across all grid cells and assigns it to the corresponding grid cell, generating the predicted point coordinates (x, y) in the CGCS2000 coordinate system, which uses east longitude and north latitude.

After predicting the class probabilities, Non-Maximum Suppression (NMS) is performed to eliminate redundant anchor boxes. The algorithm identifies the bounding box with the next highest class probability and repeats the same process until all distinct bounding boxes remain. The algorithm outputs the required elements and displays the details of the bounding boxes for each class.

A portion of samples is selected for recognition accuracy verification. Samples with an accuracy score lower than 0.6 are considered recognition errors and marked as FALSE; samples with an accuracy score between 0.8 and 1 are classified as general samples, while those with an accuracy score greater than or equal to 0.6 are considered correctly recognized and marked as TRUE. Data is included or excluded based on whether the recognition result is correct or incorrect, and correctly recognized points are classified as the unauthorized construction category. Finally, information such as the coordinates and problem types of the included points is automatically uploaded to the intelligent supervision platform for unauthorized construction, resulting in the remote sensing monitoring and recognition dataset for unauthorized construction in Nanxun District.

搜集汇总

背景与挑战

背景概述

该数据集包含508条南浔区私搭乱建遥感监测识别数据，每季度更新，采用xlsx格式，通过YOLO算法对无人机航拍影像进行目标检测，自动识别问题点位的坐标、类型和准确度，用于高效精准监测和网格化管理，提升巡查效率并节约人工成本。