mlsanityData to 7 Oct 2026

DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote Sensing

arXiv 2201.01047 ↗First posted 4 Jan 2022cs.CV eess.IVcited by 17

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Abstract (arXiv)

We propose in this article to build up a collaboration between a deep neural network and a human in the loop to swiftly obtain accurate segmentation maps of remote sensing images. In a nutshell, the agent iteratively interacts with the network to correct its initially flawed predictions. Concretely, these interactions are annotations representing the semantic labels. Our methodological contribution is twofold. First, we propose two interactive learning schemes to integrate user inputs into deep neural networks. The first one concatenates the annotations with the other network's inputs. The second one uses the annotations as a sparse ground-truth to retrain the network. Second, we propose an active learning strategy to guide the user towards the most relevant areas to annotate. To this purpose, we compare different state-of-the-art acquisition functions to evaluate the neural network uncertainty such as ConfidNet, entropy or ODIN. Through experiments on three remote sensing datasets, we show the effectiveness of the proposed methods. Notably, we show that active learning based on uncertainty estimation enables to quickly lead the user towards mistakes and that it is thus relevant to guide the user interventions.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 25 of its 54 references that are in the map. First posted on arXiv 4 Jan 2022.

Builds on

25 of 54 references.

References · 25

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2016The Cityscapes Dataset for Semantic Urban Scene Understandingcited by 4,427
  3. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  4. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,362
  5. 2016iCaRL: Incremental Classifier and Representation Learningcited by 2,313
  6. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,472
  7. 2017Deep Bayesian Active Learning with Image Datacited by 812
  8. 2019Learning Loss for Active Learningcited by 335
  9. 2018Fully Convolutional Siamese Networks for Change Detectioncited by 305
  10. 2015What's the Point: Semantic Segmentation with Point Supervisioncited by 297
  11. 2018Learning Confidence for Out-of-Distribution Detection in Neural Networkscited by 245
  12. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 212
13 more
  1. 2019Addressing Failure Prediction by Learning Model Confidencecited by 183
  2. 2016Deep Interactive Object Selectioncited by 160
  3. 2020Confidence-Aware Learning for Deep Neural Networkscited by 113
  4. 2020Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysiscited by 61
  5. 2019Continuous Adaptation for Interactive Object Segmentation by Learning from Correctionscited by 36
  6. 2018Interactive Full Image Segmentation by Considering All Regions Jointlycited by 34
  7. 2021Semantic Segmentation of Remote Sensing Images with Sparse Annotationscited by 30
  8. 2020Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Studycited by 28
  9. 2019Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenescited by 23
  10. 2019Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learningcited by 22
  11. 2019Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detectioncited by 10
  12. 2020Deep Active Learning in Remote Sensing for data efficient Change Detectioncited by 10
  13. 2020Interactive Learning for Semantic Segmentation in Earth Observationcited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 4; 2025: 3; 2026: 5.

17 papers.

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  1. 2023An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mappingcited by 8
  2. 2022Deep Active Learning for Multi-Label Classification of Remote Sensing Imagescited by 6
  3. 2023Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planningcited by 6
  4. 2023Correlation-aware active learning for surgery video segmentationcited by 4
  5. 2024Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Imagescited by 4
  6. 2024SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarioscited by 3
  7. 2024Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentationcited by 2
  8. 2025SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contextscited by 1
  9. 2025Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutionscited by 1
  10. 2026Survey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Modelscited by 1
  11. 2023Active Label Refinement for Semantic Segmentation of Satellite Imagescited by 0
  12. 2024Weakly Supervised Framework Considering Multi-temporal Information for Large-scale Cropland Mapping with Satellite Imagerycited by 0
5 more
  1. 2025Hierarchical Semi-Supervised Active Learning for Remote Sensingcited by 0
  2. 2026ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentationcited by 0
  3. 2026Dynamic Class-Aware Active Learning for Unbiased Satellite Image Segmentationcited by 0
  4. 2026iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervisioncited by 0
  5. 2026FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discoverycited by 0