Multimodal Remote Sensing Images for Cross-Modal Object Detection

A 53.3 MB collection of TIF and DOCX files, this dataset supports research on object detection with incomplete multimodal remote sensing data. It was contributed by author Hongjun Ma and last updated in April 2026. The data was used to validate a proposed cross-modal contrastive learning and knowledge distillation method.

Use Cases

• Training cross-modal contrastive learning models to align features from easy-to-detect and hard-to-detect remote sensing modalities.
• Evaluating knowledge distillation strategies for transferring detection performance from a teacher network to a student network using the provided TIF images.
• Benchmarking object detection methods under incomplete modality scenarios using the three standard datasets referenced in the documentation.
• Implementing the proposed category-constrained contrastive learning mechanism to improve intraclass compactness and interclass separability in the feature space.

Strengths

• Dataset size is 53.3 MB, containing TIF image files and DOCX documentation.
• Validated against three standard object detection datasets for method comparison.
• Released under a permissive CC-BY-4.0 license for open use.

Limitations

• Small scale at 53.3 MB, limiting the number of training samples for deep learning models.
• Specific row counts, geographic coverage, and temporal range for the images are not provided.
• Relies on the described method paper for context; raw data labels and annotations are not detailed.

Provenance

Source

Hongjun Ma, via the figshare repository.

Collection Method

Created to validate the proposed CCLKD (cross-modal contrastive learning and knowledge distillation) method.

Freshness

Last updated in April 2026, indicating recent curation.