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

arXiv: https://arxiv.org/abs/2201.01047 · HTML: https://mlsanity.com/paper/2201.01047/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 54 references that are in the map.
- First posted on arXiv 4 Jan 2022.

## Builds on (most cited first, 25 of 25)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks](https://mlsanity.com/paper/1610.02136/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Learning Confidence for Out-of-Distribution Detection in Neural Networks](https://mlsanity.com/paper/1802.04865/) (2018)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [Addressing Failure Prediction by Learning Model Confidence](https://mlsanity.com/paper/1910.04851/) (2019)
- [Deep Interactive Object Selection](https://mlsanity.com/paper/1603.04042/) (2016)
- [Confidence-Aware Learning for Deep Neural Networks](https://mlsanity.com/paper/2007.01458/) (2020)
- [Agriculture-Vision: A Large Aerial Image Database for Agricultural Pattern Analysis](https://mlsanity.com/paper/2001.01306/) (2020)
- [Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections](https://mlsanity.com/paper/1911.12709/) (2019)
- [Interactive Full Image Segmentation by Considering All Regions Jointly](https://mlsanity.com/paper/1812.01888/) (2018)
- [Semantic Segmentation of Remote Sensing Images with Sparse Annotations](https://mlsanity.com/paper/2101.03492/) (2021)
- [Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study](https://mlsanity.com/paper/2010.07830/) (2020)
- [Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes](https://mlsanity.com/paper/1904.09092/) (2019)
- [Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning](https://mlsanity.com/paper/1907.07319/) (2019)
- [Guided Anisotropic Diffusion and Iterative Learning for Weakly Supervised Change Detection](https://mlsanity.com/paper/1904.08208/) (2019)
- [Deep Active Learning in Remote Sensing for data efficient Change Detection](https://mlsanity.com/paper/2008.11201/) (2020)
- [Interactive Learning for Semantic Segmentation in Earth Observation](https://mlsanity.com/paper/2009.11250/) (2020)

## Built on by (most cited first, 17 of 17)

- [An Informative Path Planning Framework for Active Learning in UAV-based Semantic Mapping](https://mlsanity.com/paper/2302.03347/) (2023)
- [Deep Active Learning for Multi-Label Classification of Remote Sensing Images](https://mlsanity.com/paper/2212.01165/) (2022)
- [Semi-Supervised Active Learning for Semantic Segmentation in Unknown Environments Using Informative Path Planning](https://mlsanity.com/paper/2312.04402/) (2023)
- [Correlation-aware active learning for surgery video segmentation](https://mlsanity.com/paper/2311.08811/) (2023)
- [Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Images](https://mlsanity.com/paper/2404.11299/) (2024)
- [SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios](https://mlsanity.com/paper/2408.04482/) (2024)
- [Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation](https://mlsanity.com/paper/2412.06470/) (2024)
- [SkipClick: Combining Quick Responses and Low-Level Features for Interactive Segmentation in Winter Sports Contexts](https://mlsanity.com/paper/2501.07960/) (2025)
- [Data-Centric AI for Tropical Agricultural Mapping: Challenges, Strategies and Scalable Solutions](https://mlsanity.com/paper/2510.16207/) (2025)
- [Survey on Remote Sensing Scene Classification: From Traditional Methods to Large Generative AI Models](https://mlsanity.com/paper/2603.26751/) (2026)
- [Active Label Refinement for Semantic Segmentation of Satellite Images](https://mlsanity.com/paper/2309.06159/) (2023)
- [Weakly Supervised Framework Considering Multi-temporal Information for Large-scale Cropland Mapping with Satellite Imagery](https://mlsanity.com/paper/2411.18475/) (2024)
- [Hierarchical Semi-Supervised Active Learning for Remote Sensing](https://mlsanity.com/paper/2511.18058/) (2025)
- [ActiveFreq: Integrating Active Learning and Frequency Domain Analysis for Interactive Segmentation](https://mlsanity.com/paper/2603.11498/) (2026)
- [Dynamic Class-Aware Active Learning for Unbiased Satellite Image Segmentation](https://mlsanity.com/paper/2604.08965/) (2026)
- [iSAGE: A Human-in-the-Loop Framework for Remote Sensing Semantic Segmentation via Sparse Point Supervision](https://mlsanity.com/paper/2606.10136/) (2026)
- [FunnelAL: Retrieve-then-Rank Active Learning for Single-Class Discovery](https://mlsanity.com/paper/2607.25276/) (2026)

## 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.