Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Remote Sensing Vision-Language Foundation Models without Annotations via Ground Remote Alignment
Authors: Utkarsh Mall, Cheng Perng Phoo, Meilin Kelsey Liu, Carl Vondrick, Bharath Hariharan, Kavita Bala
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 EXPERIMENTS AND RESULTS |
| Researcher Affiliation | Academia | 1Cornell University, Ithaca, NY 2Columbia University, New York, NY |
| Pseudocode | No | The paper describes methods in text and equations but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code, data, and other resources are available at: https://graft.cs.cornell.edu |
| Open Datasets | Yes | To perform our training, we need a dataset of ground-satellite image pairs. We collected two such datasets for two different kinds of remote sensing imagery: NAIP (U.S.G.S., 2022) (high resolution, with 1 meter per pixel) and Sentinel-2 (Drusch et al., 2012) (low resolution, with 10 meters per pixel). Our dataset collection efforts yield 10.2 million pairs for NAIP and 8.7 million pairs for Sentinel-2 (also refer to Appendix A). |
| Dataset Splits | Yes | We select hyperparameters using a validation set with NAIP resolution that we collected. This validation set contains 14 categories with a total of 2632 single-label images. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software components like Vi T-B/16 and Adam W, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train all models for 10 epochs using Adam W with weight decay set to 1e-2. For image-level model, we linearly ramp up the learning rate from 0 to 1e-5 and then decrease the learning rate using a cosine schedule. For pixel-level model, we linearly ramp up the learning rate from 0 to to 5e-5 and then decrease the learning rate to zero using a cosine schedule. All models are initialized using CLIP s weights and the temperature hyperparameter is set to τ = 0.07. |