DeepShade: Outdoor Scenes with Shade Conditions for Text-Conditioned Image Generation

DeepShade is a multimodal dataset designed for supervised training of diffusion-based models to simulate sun-shade transitions. It captures realistic outdoor scenes and their corresponding shade conditions over time, based on spatial layout and temporal context. The dataset was introduced by DARL-ASU for an IJCAI 2025 submission and was last updated on July 17, 2025.

Use Cases

• Train diffusion models for text-conditioned image generation based on scene descriptions.
• Simulate sun-shade transitions in outdoor scenes based on spatial layout data.
• Generate images with realistic shading based on temporal context information.
• Conduct research on multimodal learning combining text, image, and environmental condition data.

Strengths

• Designed for supervised training of diffusion-based models, indicating a structured learning objective.
• Captures shade conditions over time, providing temporal context.
• Introduced in a formal research submission (IJCAI 2025), suggesting academic rigor.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and license are unknown, which may limit suitability assessment.
• Data may reflect geographic or scene bias inherent to its collection method.

Provenance

Source

DARL-ASU

Collection Method

Likely gathered via capture or simulation of outdoor scenes and shade conditions.

Time Range

Temporal context is captured, but specific date range is unknown.

Freshness

Last updated 2025-07-17 19:14:23; freshness should be verified.

Geography

Outdoor scenes, but specific geographic coverage is unknown.