arxiv_cv 85% Match Research Paper Remote sensing analysts,Computer vision researchers,AI ethicists,Geospatial data scientists 2 months ago

Checkmate: interpretable and explainable RSVQA is the endgame

computer-vision › scene-understanding

📄 Abstract

Abstract: Remote Sensing Visual Question Answering (RSVQA) presents unique challenges in ensuring that model decisions are both understandable and grounded in visual content. Current models often suffer from a lack of interpretability and explainability, as well as from biases in dataset distributions that lead to shortcut learning. In this work, we tackle these issues by introducing a novel RSVQA dataset, Chessboard, designed to minimize biases through 3'123'253 questions and a balanced answer distribution. Each answer is linked to one or more cells within the image, enabling fine-grained visual reasoning. Building on this dataset, we develop an explainable and interpretable model called Checkmate that identifies the image cells most relevant to its decisions. Through extensive experiments across multiple model architectures, we show that our approach improves transparency and supports more trustworthy decision-making in RSVQA systems.

Key Contributions

Introduces the Chessboard dataset for RSVQA, designed to minimize bias with a balanced answer distribution and linked image cells for fine-grained reasoning. Develops the Checkmate model, which enhances interpretability and explainability by identifying relevant image cells for its decisions, leading to more trustworthy RSVQA systems.

Business Value

Enables more reliable and understandable analysis of remote sensing imagery, crucial for applications like environmental monitoring, urban planning, and disaster response where understanding model reasoning is critical.

Paper Metadata

Innovation Type

Dataset and Model

Deployment Feasibility

Moderate. Requires specialized remote sensing data and computational resources for training and inference.

Limitations Addressed

Lack of interpretability and explainability in RSVQA models, dataset biases leading to shortcut learning.

Performance Gains

Improved transparency and support for more trustworthy decision-making.

Technical Tags

RSVQARemote SensingVisual Question AnsweringInterpretabilityExplainabilityBias MitigationShortcut LearningFine-grained Visual ReasoningDataset DesignDeep Learning

Research Topics

Explainable AI (XAI)Computer VisionRemote Sensing Data AnalysisDataset CurationModel Interpretability

Methods & Architectures

Dataset creationExplainable model developmentFine-grained visual reasoningBias reduction techniques Multiple model architectures

Applications & Tasks

Remote Sensing Geospatial Analysis Environmental Monitoring Urban Planning Disaster Management Lack of interpretabilityLack of explainabilityDataset biasShortcut learning Remote Sensing Visual Question Answering (RSVQA)Visual reasoningDecision transparency

Datasets & Benchmarks

Datasets

Chessboard

TransparencyTrustworthiness

Related Fields

Computer VisionNatural Language ProcessingExplainable AIRemote Sensing

Keywords

RSVQARemote SensingVisual Question AnsweringInterpretabilityExplainabilityBiasShortcut LearningFine-grained ReasoningDatasetDeep LearningComputer VisionGeospatial AI

Academic Context

#Explainable AI (XAI)#Computer Vision#Remote Sensing Data Analysis#Dataset Curation#Model Interpretability

Commercial Potential

Potential Products

Interpretable remote sensing analysis toolsExplainable AI modules for geospatial platforms

Target Industries

Geospatial intelligenceEnvironmental monitoringUrban planningDefense and securityAgriculture

Use Case Examples

Answering questions about land use changes from satellite imagery with explanationsIdentifying deforestation patterns with model reasoning

Competitive Edge

Offers a more interpretable and less biased alternative to existing RSVQA models, addressing key limitations in trustworthiness and understanding.

Market Opportunity

Growing market for AI in geospatial analysis and remote sensing.

Revenue Models

SaaS for analysis platformslicensing of models/datasets.

Resource Requirements

Compute Needs

Requires significant GPU resources for training deep learning models on large image datasets.

Data Requirements

Large, diverse remote sensing image datasets with question-answer pairs and cell-level annotations.

Deployment Constraints

Need for high-quality remote sensing data, computational resources for inference.

Scalability

Scalability depends on the underlying model architecture and the ability to process large volumes of remote sensing data.

Production Readiness

Maturity Level

Research Prototype

Time to Market

1-3 years

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