Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing

• URL: http://arxiv.org/abs/2411.14119v1
• Date: Thu, 21 Nov 2024 13:42:24 GMT
• Title: Uncertainty-Aware Regression for Socio-Economic Estimation via Multi-View Remote Sensing
• Authors: Fan Yang, Sahoko Ishida, Mengyan Zhang, Daniel Jenson, Swapnil Mishra, Jhonathan Navott, Seth Flaxman,
• Abstract summary: We introduce a novel framework to process remote sensing imagery using combinations of three spectral bands. Our framework helps identify uncertain predictions, guiding future ground truth data acquisition.
• Score: 4.787295791127483
• License:
• Abstract: Remote sensing imagery offers rich spectral data across extensive areas for Earth observation. Many attempts have been made to leverage these data with transfer learning to develop scalable alternatives for estimating socio-economic conditions, reducing reliance on expensive survey-collected data. However, much of this research has primarily focused on daytime satellite imagery due to the limitation that most pre-trained models are trained on 3-band RGB images. Consequently, modeling techniques for spectral bands beyond the visible spectrum have not been thoroughly investigated. Additionally, quantifying uncertainty in remote sensing regression has been less explored, yet it is essential for more informed targeting and iterative collection of ground truth survey data. In this paper, we introduce a novel framework that leverages generic foundational vision models to process remote sensing imagery using combinations of three spectral bands to exploit multi-spectral data. We also employ methods such as heteroscedastic regression and Bayesian modeling to generate uncertainty estimates for the predictions. Experimental results demonstrate that our method outperforms existing models that use RGB or multi-spectral models with unstructured band usage. Moreover, our framework helps identify uncertain predictions, guiding future ground truth data acquisition.

Related papers

• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)
• RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic Observations [18.23500204496233]
  The Realistic Anomaly Detection dataset (RAD) is the first multi-view RGB-based anomaly detection dataset specifically collected using a real robot arm. RAD comprises 4765 images across 13 categories and 4 defect types, collected from more than 50 viewpoints. We propose a data augmentation method to improve the accuracy of pose estimation and facilitate the reconstruction of 3D point clouds.
  arXiv  Detail & Related papers  (2024-10-01T14:05:35Z)
• Thinking Racial Bias in Fair Forgery Detection: Models, Datasets and Evaluations [63.52709761339949]
  We first contribute a dedicated dataset called the Fair Forgery Detection (FairFD) dataset, where we prove the racial bias of public state-of-the-art (SOTA) methods. We design novel metrics including Approach Averaged Metric and Utility Regularized Metric, which can avoid deceptive results. We also present an effective and robust post-processing technique, Bias Pruning with Fair Activations (BPFA), which improves fairness without requiring retraining or weight updates.
  arXiv  Detail & Related papers  (2024-07-19T14:53:18Z)
• RANRAC: Robust Neural Scene Representations via Random Ray Consensus [12.161889666145127]
  RANdom RAy Consensus (RANRAC) is an efficient approach to eliminate the effect of inconsistent data. We formulate a fuzzy adaption of the RANSAC paradigm, enabling its application to large scale models. Results indicate significant improvements compared to state-of-the-art robust methods for novel-view synthesis.
  arXiv  Detail & Related papers  (2023-12-15T13:33:09Z)
• Autoregressive Uncertainty Modeling for 3D Bounding Box Prediction [63.3021778885906]
  3D bounding boxes are a widespread intermediate representation in many computer vision applications. We propose methods for leveraging our autoregressive model to make high confidence predictions and meaningful uncertainty measures. We release a simulated dataset, COB-3D, which highlights new types of ambiguity that arise in real-world robotics applications.
  arXiv  Detail & Related papers  (2022-10-13T23:57:40Z)
• Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things [82.15959827765325]
  We propose a novel approach to multimodal sensor fusion for Ambient Assisted Living (AAL) We address two major shortcomings of standard multimodal approaches, limited area coverage and reduced reliability. Our new framework fuses the concept of modality hallucination with triplet learning to train a model with different modalities to handle missing sensors at inference time.
  arXiv  Detail & Related papers  (2022-07-14T10:04:18Z)
• Statistics and Deep Learning-based Hybrid Model for Interpretable Anomaly Detection [0.0]
  Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at both forecasting tasks. MES-LSTM is an interpretable anomaly detection model that overcomes these challenges.
  arXiv  Detail & Related papers  (2022-02-25T14:17:03Z)
• The Lifecycle of a Statistical Model: Model Failure Detection, Identification, and Refitting [26.351782287953267]
  We develop tools and theory for detecting and identifying regions of the covariate space (subpopulations) where model performance has begun to degrade. We present empirical results with three real-world data sets. We complement these empirical results with theory proving that our methodology is minimax optimal for recovering anomalous subpopulations.
  arXiv  Detail & Related papers  (2022-02-08T22:02:31Z)
• A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration [36.525810477650026]
  Hyperspectral imaging offers new perspectives for diverse applications. The lack of accurate ground-truth "clean" hyperspectral signals on the spot makes restoration tasks challenging. In this paper, we advocate for a hybrid approach based on sparse coding principles.
  arXiv  Detail & Related papers  (2021-11-18T14:16:04Z)
• Residual Overfit Method of Exploration [78.07532520582313]
  We propose an approximate exploration methodology based on fitting only two point estimates, one tuned and one overfit. The approach drives exploration towards actions where the overfit model exhibits the most overfitting compared to the tuned model. We compare ROME against a set of established contextual bandit methods on three datasets and find it to be one of the best performing.
  arXiv  Detail & Related papers  (2021-10-06T17:05:33Z)
• When Relation Networks meet GANs: Relation GANs with Triplet Loss [110.7572918636599]
  Training stability is still a lingering concern of generative adversarial networks (GANs) In this paper, we explore a relation network architecture for the discriminator and design a triplet loss which performs better generalization and stability. Experiments on benchmark datasets show that the proposed relation discriminator and new loss can provide significant improvement on variable vision tasks.
  arXiv  Detail & Related papers  (2020-02-24T11:35:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.