Sijie Zhao

Remote sensing models have long been built by hand, with experts designing a training recipe for each new task and tuning it against a benchmark. RSI4EO casts a task as two parts: a harness that fixes the data, pretrained weights and compute an agent may use, and a sealed verifier that returns only a score. A general-purpose coding agent then writes the training code, trains models and improves them recursively. On four benchmarks with published results, spanning optical, SAR and multispectral time-series imagery, the agent-built models surpass the state of the art, and with about 1% of the training labels they keep most of that accuracy.

Remote sensing models have long been built by hand, with experts designing a training recipe for each new task and tuning it against a benchmark. RSI4EO casts a task as two parts: a harness that fixes the data, pretrained weights and compute an agent may use, and a sealed verifier that returns only a score. A general-purpose coding agent then writes the training code, trains models and improves them recursively. On four benchmarks with published results, spanning optical, SAR and multispectral time-series imagery, the agent-built models surpass the state of the art, and with about 1% of the training labels they keep most of that accuracy.