Large Multimodal Models (LMMs) has demonstrated capabilities across various domains, but comprehensive benchmarks for agricultural remote sensing (RS) remain scarce. Existing benchmarks designed for agricultural RS scenarios exhibit notable limitations, primarily in terms of insufficient scene diversity in the dataset and oversimplified task design. To bridge this gap, we introduce AgroMind, a comprehensive agricultural remote sensing benchmark covering four task dimensions: spatial perception, object understanding, scene understanding, and scene reasoning, with a total of 13 task types, ranging from crop identification and health monitoring to environmental analysis. We curate a high-quality evaluation set by integrating nine public datasets and one private global parcel dataset, containing 28,482 QA pairs and 20,850 images. The pipeline begins with multi-source data pre-processing, including collection, format standardization, and annotation refinement. We then generate a diverse set of agriculturally relevant questions through the systematic definition of tasks. Finally, we employ LMMs for inference, generating responses, and performing detailed examinations. We evaluated 20 open-source LMMs and 4 closed-source models on AgroMind. Experiments reveal significant performance gaps, particularly in spatial reasoning and fine-grained recognition, it is notable that human performance lags behind several leading LMMs. By establishing a standardized evaluation framework for agricultural RS, AgroMind reveals the limitations of LMMs in domain knowledge and highlights critical challenges for future work. Data and code can be accessed at https://rssysu.github.io/AgroMind/.

In the field of remote sensing image classification, domain adaptation (DA) methods have been extensively utilized to overcome the challenges posed by data discrepancies between source and target domains that arise from varying imaging conditions, sensor differences, or geographical variations. Stemming from the existence of unseen classes in both the source and target domains, universal DA (UniDA) poses the greatest challenge that demands innovative solutions. Existing UniDA methods often overlook intra-domain variations within the target domain and face difficulties in distinguishing between similar known and unknown classes, which significantly hinder cross-domain transfer. To overcome these challenges, we propose a dual-classifier network tailored for cross-domain classification of remote sensing images, namedDCmix. DCmix introduces a dual-classifiers network that utilizes both closed-set and open-set classifiers to improve the accuracy of identifying unknown sample classes. To our knowledge, this is the first attempt to introduce dual classifiers into the UniDA remote sensing image classification task. We further enhance the feature generalization capability of the target domain based on sample neighborhood relations, resulting in a more adaptable and robust feature representation. A cross-domain feature mixup scheme is also designed based on the consistency discrimination of the dual classifiers, achieving smoother decision boundaries and simpler hidden layer representations. Extensive experiments conducted on four hyperspectral image datasets and three RGB datasets prove that the introduced approach attains state-of-the-art performance in remote sensing image classification under the UniDA scenario.

Although enormous Domain Adaptation (DA) approaches have been proposed for cross-scene hyperspectral image (HSI) classification, most of them strongly rely on prior knowledge of the relationship between the label sets of source and target domains (including closed-set, partial and open-set DA), which significantly limits their applications. In a real-world application scenario, we often transfer knowledge between domains without any constraints on the label sets, which is called Universal Domain Adaptation (UniDA). In this paper, we propose HyUniDA, which is the first attempt to address UniDA scenario from HSIs. HyUniDA contains two major parts: the Shared Semantic Pairing (SSP) and Domain Similarity Score (DSS). The SSP identifies pairs of clusters that have coincident semantic features as the common classes. By examining the consistency level of samples across source and target domains, DSS can estimate the number of target clusters and generate distinct clusters without prior knowledge. We evaluate our proposed method on two transfer learning tasks for four typical HSI datasets, it turns out that our proposed method yields 6.41%∼34.71% improvements compared to other state-of-the-art DA methods.

Although enormous Domain Adaptation (DA) approaches have been proposed for cross-scene hyperspectral image (HSI) classification, majority DA methods strongly depend on much prior knowledge of the association among the label sets of source and target domains (encompassing closed set, partial and open set DA), thereby significantly hindering their applications. Realistic application scenarios often require knowledge transfer between domains without restrictions on the label space, which is called Universal Domain Adaptation (UniDA). In this paper, we propose HyUniDA, which is the first attempt to address UniDA scenario from HSIs. HyUniDA contains two major parts: the Shared Semantic Pairing (SSP) and Domain Similarity Score (DSS). We group both source and target domains to form discriminative clusters. The SSP identifies pairs of clusters that have coincident semantic features as the common classes. By examining the consistency level of samples across source and target domains, DSS can estimate the quantity of target clusters and generate distinct clusters without prior knowledge. Meanwhile, we apply the contrastive domain discrepancy to alleviate the offset of samples distribution, with a representative regularizer to assist distinguish target domain clusters. We evaluate our proposed method on three transfer learning tasks for six typical HSI datasets, it turns out that our proposed method yields 3.83%~37.57% improvements compared to other state-of-the-art DA methods.

UAV (Unmanned Aircraft Vehicle) could undertake rescue, traffic, reconnaissance in indoor environment. As the key of indoor autonomous flight and intelligent flight, UAV indoor path planning has great research significance. UAV path planning is to design an optimal feasible path in flight airspace given the UAV related constraints and cost functions. As a special kind of airspace environment, the building environment has complex internal environment and dense obstacles, so the UAV path planning is required to complete the operation quickly and effectively, and ensure the global optimization of the path. Based on the global three-dimensional subdivision reference grid system, this paper will establish the environment model of the spatial grid inside the building, and develop a new path planning algorithm based on the indoor spatial grid. Ultimately, the UAV will complete the path planning task quickly and efficiently inside the building.