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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.

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Where it is published

Catalogue records · 1

Topics

Inferred from text
Image 75%
Provenance · 1 source records, 12 field assertions
SourceKeyLast seenRaw
DANS Data Station Physical and Technical Sciencesdoi:10.17026/PT/91K0SS12 d agoJSON v1
FieldAssertionExtractorEvidence
concepts[field].dataverse_subject:computer-and-information-sciencesource · phys techsciences datastations nlconnector:phys_techsciences_datastations_nl@1.0.0/subjects
concepts[field].dataverse_subject:earth-and-environmental-sciencessource · phys techsciences datastations nlconnector:phys_techsciences_datastations_nl@1.0.0/subjects
concepts[field].local:field:computer-science-aimapping · phys techsciences datastations nlconnector:phys_techsciences_datastations_nl@1.0.0/subjects
concepts[field].local:field:earth-environmentalmapping · phys techsciences datastations nlconnector:phys_techsciences_datastations_nl@1.0.0/subjects
concepts[modality].local:modality:imageenrichment · phys techsciences datastations nlkeyword-concept-rules@1.0.0title+description (75%)
concepts[modality].local:modality:remote-sensingmapping · phys techsciences datastations nlvocabulary-mapper@1.0.0keywords['Earth Observation']
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