Abstract
Semantic segmentation of aerial LiDAR point clouds underpins applications in urban planning, environmental monitoring, and disaster management. A thorough comparative study of two leading point-based architectures, RandLANet and PointNet++, has been conducted using the DALES dataset. Quantitative evaluation includes Global Accuracy, Mean Accuracy, Mean IoU, and per-class IoU, supported by an expansive literature review of 18 foundational and recent works, ablation studies on block partitioning and sampling strategies, and computational trade-off analysis. RandLANet attains 95.78% Global Accuracy and 0.702 Mean IoU, outperforming PointNet++ (93.65%, 0.534). These findings guide the selection of point-wise segmentation models for large-scale airborne LiDAR.
| Original language | English |
|---|---|
| Title of host publication | 2025 Control Instrumentation System Conference, CISCON 2025 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798331597733 |
| DOIs | |
| Publication status | Published - 2025 |
| Event | 2025 Control Instrumentation System Conference, CISCON 2025 - Hybrid, Bangalore, India Duration: 01-08-2025 → 02-08-2025 |
Publication series
| Name | 2025 Control Instrumentation System Conference, CISCON 2025 |
|---|
Conference
| Conference | 2025 Control Instrumentation System Conference, CISCON 2025 |
|---|---|
| Country/Territory | India |
| City | Hybrid, Bangalore |
| Period | 01-08-25 → 02-08-25 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 11 Sustainable Cities and Communities
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
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