德清县生态公墓遥感监测识别数据

用于实现对德清县生态公墓遥感监测中墓地点位的高效精准识别，主要识别内容为民政数字化治理中的公益性墓地与经营性公墓。算法通过墓地点位在影像中的相对位置，自动计算出现有墓地的实地坐标、面积等信息。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生态公墓遥感影像数据，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2024年8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生态公墓类别。最后将纳入的点位坐标、问题类型等信息自动上传至生态公墓智能监管平台，获得德清县生态公墓遥感监测识别数据。

This dataset is developed for efficient and accurate identification of cemetery points in remote sensing monitoring of ecological cemeteries in Deqing County. The primary targets of identification are public welfare cemeteries and commercial cemeteries involved in the digital governance of civil affairs.
Based on the relative positions of cemetery points in the images, the algorithm automatically calculates the field coordinates, area and other relevant information of existing cemeteries. It facilitates subsequent personnel management and issue assignment, enabling grid managers to timely grasp the status of their managed regions. The dataset addresses the pain points of traditional manual patrols conducted by grid managers, including difficulty in detecting problems, high probability of omitting issues, overly broad patrol scope, and inconvenience in patrolling hidden areas, remote regions and harsh environments.
By assigning the problem points identified by the system to grid managers, it allows them to conduct on-site verification with clear objectives and evidence, greatly improving the efficiency of problem detection, saving labor time and costs, and eliminating invalid patrols for grid managers.
The remote sensing image data of ecological cemeteries is collected via UAV aerial photography, and real-time object detection is performed using the YOLO algorithm. First, the cell neural network is applied to the remote sensing images captured in August 2024, where the images are split into 19×19 grid cells, and each cell is responsible for predicting K bounding boxes. The probability of each region is predicted, and the class with the highest probability across all grid cells is selected and assigned to a specific grid cell, generating the predicted point coordinates (x, y). The coordinate system adopted is CGCS2000, with coordinates expressed as east longitude and north latitude.
After predicting the class probabilities, the Non-Maximum Suppression (NMS) operation is carried out 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 are retained. Finally, the algorithm outputs the required features and displays the detailed information of the bounding boxes for each class.
A subset of samples is selected for recognition accuracy verification. Samples with a confidence score lower than 0.6 are deemed recognition errors and marked as FALSE; general samples have a recognition accuracy ranging from 0.8 to 1, and samples with a confidence score greater than 0.6 are considered correctly recognized and marked as TRUE. Data is included or excluded based on the correctness of the recognition result, and correctly identified points are categorized as ecological cemeteries.
Finally, the information such as the coordinates and problem types of the included points is automatically uploaded to the ecological cemetery intelligent supervision platform, thus obtaining the remote sensing monitoring and identification data of ecological cemeteries in Deqing County.

搜集汇总

背景与挑战

背景概述

该数据集是德清县生态公墓的遥感监测识别数据，包含511条记录，每年更新，采用xlsx格式存储，涵盖坐标、种类和识别准确度等字段。它基于YOLO算法对无人机航拍影像进行目标检测，自动识别公益性墓地和经营性公墓，用于民政数字化治理，提高巡查效率和降低成本。