Data · dataset · 2025
Multi-Modal Earth Observation and Deep Learning for Urban Scene Understanding
Listed in DANS Data Station Physical and Technical Sciences
📌 This repository is a collection of all sub-repositories each of which has been been developed to solve a specific objective set by this research.
Description
The repositories are organized as follows: LabelNoise: Implementatoin to simulate of label noise and then prepare the dataset for training and comparison. SegHead: Efficient semantic segmentation Head with lower memory footprint and compute requirement without comprimising model performance.
TransFusion: Implementation TranFusion, a transformer based multimodal network for fusing Image and 3D point cloud further improving 2D semaantic segmentation. SOTA-Test: This repository contains implementations of various SOTA models for quantitative comparison and benchmarking purposes.
Links
Where it is published
- Dataverse dataset page phys-techsciences.datastations.nl/dataset.xhtml?persistentId=doi%3A10.17026%2FPT%2F91K0SS ↗
landing page · from phys techsciences datastations nl
- DOI doi.org/10.17026/pt/91k0ss ↗
DOI / persistent id · from phys techsciences datastations nl
Catalogue records · 1
- Dataverse API phys-techsciences.datastations.nl/api/datasets/:persistentId/?persistentId=doi%3A10.17026%2FPT%2… ↗
metadata API · from phys techsciences datastations nl
Topics
- Stated by source
- Computer and Information Science · Earth and Environmental Sciences
- From keywords
- Computer Science & AI · Computer vision · Deep learning · Earth & Environmental Science · Satellite remote sensing
- Inferred from text
- Image 75%
Provenance · 1 source records, 12 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DANS Data Station Physical and Technical Sciences | doi:10.17026/PT/91K0SS | 12 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].dataverse_subject:computer-and-information-science | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].dataverse_subject:earth-and-environmental-sciences | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:computer-science-ai | mapping · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /subjects |
| concepts[modality].local:modality:image | enrichment · phys techsciences datastations nl | keyword-concept-rules@1.0.0 | title+description (75%) |
| concepts[modality].local:modality:remote-sensing | mapping · phys techsciences datastations nl | vocabulary-mapper@1.0.0 | keywords['Earth Observation'] |
| created_date | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | |
| description | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /description |
| publication_date | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | |
| title | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | /name |
| updated_date | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 | |
| version_label | source · phys techsciences datastations nl | connector:phys_techsciences_datastations_nl@1.0.0 |