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Found 6 papers, 3 papers with code
Multi-task Learning for Human Settlement Extent Regression and Local Climate Zone Classification
Human Settlement Extent (HSE) and Local Climate Zone (LCZ) maps are both essential sources, e. g., for sustainable urban development and Urban Heat Island (UHI) studies.
A Generalized Multi-Task Learning Approach to Stereo DSM Filtering in Urban Areas
Such basic models can be filtered by convolutional neural networks (CNNs), trained on labels derived from digital elevation models (DEMs) and 3D city models, in order to obtain a refined DSM.
Weakly Supervised Semantic Segmentation of Satellite Images for Land Cover Mapping -- Challenges and Opportunities
Therefore, this paper seeks to make a case for the application of weakly supervised learning strategies to get the most out of available data sources and achieve progress in high-resolution large-scale land cover mapping.
Weakly-supervised Learning
Weakly supervised Semantic Segmentation
+1
MultiDepth: Single-Image Depth Estimation via Multi-Task Regression and Classification
Hence, in order to overcome the notorious instability and slow convergence of depth value regression during training, MultiDepth makes use of depth interval classification as an auxiliary task.
Auxiliary Tasks in Multi-task Learning
Multi-task convolutional neural networks (CNNs) have shown impressive results for certain combinations of tasks, such as single-image depth estimation (SIDE) and semantic segmentation.
Evaluation of CNN-based Single-Image Depth Estimation Methods
While an increasing interest in deep models for single-image depth estimation methods can be observed, established schemes for their evaluation are still limited.
