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An Active Learning-Based Estimation Method for Zootopia

An active learning and animal technology, applied in computing, computer components, instruments, etc., can solve problems such as not being able to fully take into account the influence of terrain and obstacles, not being able to take into account the influence of terrain and obstacles, and difficult to take into account the anisotropy of animal activities

Active Publication Date: 2022-03-18
TIANJIN NORMAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the traditional kernel density estimation method is based on the Euclidean distance, which cannot fully take into account the influence of terrain and obstacles on the distance between space objects.
In other words, it is difficult for these methods to account for the anisotropy of animal activities
[0007] (3) The local convex hull method, the problem of this method is that it cannot express the fuzzy boundary of the animal's range of activities
Can not take into account the impact of terrain and obstacles

Method used

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  • An Active Learning-Based Estimation Method for Zootopia
  • An Active Learning-Based Estimation Method for Zootopia
  • An Active Learning-Based Estimation Method for Zootopia

Examples

Experimental program
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Effect test

Embodiment 1

[0059] A variety of geographic information data (GPS point set, basic geographic information data, DEM data) is used as input data, among which the GPS point data set is the tracking data of animal activities; obstacles can be determined by basic geographic information data, and obstacles of different animals Animals may vary; DEM data were used to determine the impact of topography on animal activity. It should be noted that due to the diversity of animals, different animals may require different data. Rasterize the above three data with the same resolution and unify the coordinate system. Then use the cost distance calculation module to calculate the cost distance from each GPS tracking point to any point in the animal's activity area; use the possibility distribution calculation module to calculate the fuzzy probability distribution of the animal's activities in this area; use the home domain extraction module to pass The threshold is determined in advance to extract the c...

Embodiment 2

[0091] A simulated dataset such as Figure 5 Shown, the simulated animal is the sika deer, which prefers to live on the edge of forests and meadows, but not in dense forests or bushes; moreover, they prefer areas with little human disturbance, open spaces, and abundant water. The spatial resolution of the DEM data is 25m, and the elevation ranges from 689m to 2129m. In this example, the area above 1800m above sea level ( Image 6 Marked as A in ), the pond ( Image 6 Marked as B in), expressway service area ( Image 6Marked as C), expressway ( Image 6 marked D) and the farm ( Image 6 Marked as E) is considered to be an obstacle to the activities of sika deer. Since animals prefer to live on flat terrain rather than rugged and steep areas, the terrain relief obtained by DEM was used to measure the cost distance in this area. Utilize the method of the present invention, obtain the fuzzy degree of membership of the family domain of simulation data set, such as Figure 7 ...

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Abstract

The invention discloses a method for estimating animal home domains based on active learning, which takes GPS point sets, basic geographic information data, and multiple geographic information data of DEM data as input data, wherein the GPS point data set is the tracking of animal activities Data, obstacles can be determined by basic geographic information data, different animals may have different obstacles; digital elevation model (DEM) data is used to determine the impact of terrain on animal activities, and then use the cost distance calculation module to calculate each GPS tracking point The cost distance to any point in the animal's activity area; use the possibility distribution calculation module to calculate the fuzzy possibility distribution of the animal's activities in this area; use the home domain extraction module to extract the core area and non The core area (border area) realizes the calculation of the home domain.

Description

[0001] This application was supported by Tianjin Natural Science Foundation (17JCZDJC39700); National Natural Science Foundation of China (41971410). technical field [0002] The present invention relates to the modeling method of fuzzy geographic phenomenon in geographic information science and the method for determining animal home domain in ecology and animal behavior, and more specifically relates to the use of GPS tracker to obtain the position of animals, and the use of position data and basic geographic information data , DEM data, etc. to estimate the range of activities of animals. Background technique [0003] Home range (animal range) estimation is a central topic in the study of spatial ecology and is fundamental to understanding animal behavior. An animal's home range is generally defined as the percentage coverage of the range that encompasses all possible locations. Many complex factors influence the location and extent of a fauna's home range, such as an ani...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06F16/29G06K9/62G01S19/42
CPCG06Q10/04G06F16/29G01S19/42G06F18/22
Inventor 郭继发
Owner TIANJIN NORMAL UNIVERSITY
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