Language-guided object recognition in remote sensing imagery is crucial for large-scale mapping and automated data annotation. However, existing open-vocabulary and visual grounding methods rely on explicit category cues, limiting their ability to handle complex or implicit queries that require advanced reasoning. To address this issue, we introduce a new suite of tasks, including Instruction-Oriented Object Counting, Detection, and Segmentation (InstructCDS), covering open-vocabulary, open-ended, and open-subclass scenarios. We further present EarthInstruct, the first InstructCDS benchmark for earth observation. It is constructed from two diverse remote sensing datasets with varying spatial resolutions and annotation rules across 20 categories, necessitating models to interpret dataset-specific instructions. Given the scarcity of semantically rich labeled data in remote sensing, we propose InstructSAM, a training-free framework for instruction-driven object recognition. InstructSAM leverages large vision-language models to interpret user instructions and estimate object counts, employs SAM2 for mask proposal, and formulates mask-label assignment as a binary integer programming problem. By integrating semantic similarity with counting constraints, InstructSAM efficiently assigns categories to predicted masks without relying on confidence thresholds. Experiments demonstrate that InstructSAM matches or surpasses specialized baselines across multiple tasks while maintaining near-constant inference time regardless of object count, reducing output tokens by 89\% and overall runtime by over 32\% compared to direct generation approaches. We believe the contributions of the proposed tasks, benchmark, and effective approach will advance future research in developing versatile object recognition systems. The code is available at https://VoyagerXvoyagerx.github.io/InstructSAM.

Precipitation nowcasting is crucial for economic development and social life. Numerous deep learning models have recently been developed and have achieved better results than traditional extrapolation models. However, they mainly focus on improving model architectures, ignoring the impact of error accumulation and data inconsistency. This article proposes a multimodal fusion model named multimodal fusion recurrent neural network (MMF-RNN) for precipitation prediction. Specifically, we use a dual-branch encoder to extract features from radar and ground station data and then fuse them effectively through attention mechanisms and multimodal loss (ML). To address the error accumulation problem, we propose a block-based dynamic weighted loss (BDWLoss) that enables the model to focus more on hard-to-predict areas during training to reduce error accumulation. Based on BDWLoss, we propose an ML that encourages the model to maintain consistency between single-modal and fused multimodal features. In addition, MMF-RNN is compatible with various RNN models such as ConvLSTM, PredRNN, PredRNN++, and MIM. The experimental results on the RAIN-F dataset demonstrate that MMF-RNN outperforms both the single-modal model MS-RNN and the multimodal model MM-RNN. In particular, MMF-RNN achieves significant improvement in predicting heavy precipitation. Compared to MM-PredRNN++, MMF-PredRNN++ shows marked improvements across various performance metrics, with critical success index (CSI) ($R\geq 5 $) and Heidke skill score (HSS) ($R\geq 5$) increasing by 58.08% and 48.55%, respectively, and CSI ($R\geq 10$) and HSS ($R\geq 10$) showing more pronounced gains. These advancements are facilitated not only by the proposed architectural innovations but also by sample weighting, which collectively contribute to superior performance on imbalanced precipitation datasets.