O artykule
Data publikacji: 27 lut 2025
Otrzymano: 25 paź 2024
Przyjęty: 29 sty 2025
DOI: https://doi.org/10.2478/amns-2025-0111
Słowa kluczowe
© 2025 Sai Yin, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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statistical comparisons of the Wilcoxon test
IDE-BOVS | IDE | IGA |
---|---|---|
P Values | 0.008 | 0.000 |
Test set accuracy
Test set accuracy% | First, for the first time | Second time | Third time | The fourth time | Average accuracy |
---|---|---|---|---|---|
After adding the embedded layer (Single house section) | 98.54 | 99.01 | 97.63 | 99.22 | 98.60 |
After adding the embedded layer (Whole house section) | 91.40 | 90.79 | 93.31 | 93.12 | 92.16 |
Join the embedding layer before (Single house section) | 90.24 | 88.54 | 91.06 | 89.14 | 89.75 |
Join the embedding layer before (Whole house section) | 72.93 | 75.67 | 70.66 | 74.67 | 73.48 |
spatial strategy
Tactics | Merit | Shortcoming |
---|---|---|
Homogenization | Simple in structure and easy to implement | Significant loss of information and less detail |
Like meta polymerization | Retain more information and reduce the noise | May cause boundary blur and is not suitable for complex terrain |
Space averaging method | Reduce the local outliers, more smooth processing | The information is vague and can not retain the spatial characteristics |
Target selection | Important information can be selected flexibly, according to the analysis requirements | Selection criteria need to be specified, and important data may be missed |
Multiscale analysis | Be able to understand spatial phenomena from multiple perspectives | Computing complexity is high, and the integration is difficult |
Weighted assessment | Be able to consider the importance of each data source comprehensively | Weight setting requires professional knowledge and has a high degree of uncertainty |