Automatic Extraction of Indoor Spatial Information from Floor Plan Image: A Patch-Based Deep Learning Methodology Application on Large-Scale Complex Buildings
importance 4/10
ai assisted
Automated Road and Building Extraction indoor mapping stage: research
Problem
Automatic extraction of indoor spatial information from floor plans was mostly demonstrated only on small, simple, low-resolution buildings.
Why it matters
Large-scale indoor mapping is needed for practical uses such as indoor navigation in complex multi-use buildings.
What the AI did
A CNN-based framework divides floor plans into normalized patches, detects building objects per patch, and assembles and vectorizes them into one result.
Results
not stated in source
Impact
The abstract claims the framework handles varied scales and high-resolution inputs that barrier existing methods, but gives no metrics.
Record
| Venue | ISPRS International Journal of Geo-Information |
|---|---|
| Published | 2021 |
| Work type | article |
| Authors | Hyunjung Kim, Seong-Yong Kim, Kiyun Yu |
| Citations | 28 (OpenAlex, at import time) |
| References | 26 |
| Model or method | CNN (Convolutional Neural Network) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/ijgi10120828 |
| Open access | gold |
| Keywords | floor plan, CNN, deep learning, patch-based, indoor navigation, vectorization |
Importance rating
4
real but narrower advance
Practical indoor navigation in complex multi-use buildings is a real but narrower advance; abstract gives no scale or impact numbers.
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