The accurate recognition of the spatial distribution of aquaculture in coastal areas plays a crucial role in the management of natural resources and marine ecological environment protection. Using remote sensing detection method, the information of aquaculture areas can be quickly and accurately extracted from high-resolution remote sensing images. This work focuses on semantic segmentation and extraction of cage aquaculture regions using advanced deep learning algorithms. We selected Hainan Island in China as the experimental area and established the Hainan Island Offshore Cage Aquaculture Sources Dataset (HIOCASD) using high-resolution satellite remote sensing images from Gaofen-2 satellite. Six different deep learning models including DeepLabv3+, Segformer and U-Net architectures are evaluated with feature extraction via convolutional neural networks. The experimental results show that all models have excellent performance, especially U-Net model which uses VGG network as feature extractor. Through five cross-validations, its average F1-score is as high as 93.75%.

In weakly supervised semantic segmentation (WSSS) tasks on remote sensing images, it is a common practice to train a classification network from scratch using a large batch of images with a limited number of classes. Subsequently, class activation maps are extracted from the model based on predefined class indices, and these maps are then optimized to obtain pseudolabels. To make this strategy effective when introducing a new class, a substantial amount of data needs to be provided to the model. In this article, we present an innovative framework, RS-TextWS-Seg, designed to efficiently generate high-quality segmentation results for a wide range of remote sensing objects using concise descriptions. Our proposed framework comprises three sequential stages: initially, we undertake parameter fine-tuning of the contrastive language-image pretraining (CLIP) model to swiftly strengthen its capacity for zero-shot detection of a limited number of remote sensing features. Subsequently, we introduce a text-driven background suppression mechanism aimed at deriving class activation maps from the refined CLIP model based on textual cues, while concurrently mitigating background noises. Finally, we use the segment anything model (SAM) to refine the edges of the extracted class activation map. We widely researched the leading-edge methodologies in WSSS and conducted a range of comparative experiments and ablation studies to prove the efficacy of our proposed framework. The research findings underscore that RS-TextWS-Seg outperforms other state-of-the-art methods on renowned datasets such as DLRSD and Potsdam, as well as on bespoke datasets specifically curated for overground petroleum pipelines and oil well fields.

Heavy-polluting enterprises burn fossil fuels to release large amounts of greenhouse gases, causing severe pollution worldwide. Heavy-polluting enterprises have a significant responsibility for carbon emissions, and more than 130 countries have set or are considering targets for achieving net-zero carbon emissions by 2050. Assessing these enterprises can provide data support for carbon emissions and aid in evaluating industry’s economic development. In view of the problem that the existing research data is not comprehensive and the generalisation ability is week. To address this issue, we construct a high-resolution remote sensing image dataset of global heavy-polluting enterprises and use the classic target detection network SSD, Faster R-CNN and YOLOv3 for training, testing and evaluation. The experimental results findings indicate that the SSD network is particularly well-suited for object detection of heavy-polluting enterprises in the remote sensing domain.

The number and geographical location of oil well sites can reflect the local oil production situation and there is a growing interest in automatically identifying oil well sites from remote sensing images. Traditionally, visual interpretation was employed to extract oil well sites locations from remotely sensing images. However, this approach is time-consuming and heavily dependent on domain experts. Advancements in remote sensing satellite technology and the widespread use of deep learning algorithms have enabled the automated extraction of oil well sites from remote sensing images. In this paper, we established the Northeast Petroleum University Oil Well Sites Dataset Version 1.0 (NEPU-OWS V1.0), and to evaluate its usability by comparing several different deep learning models based on semantic segmentation algorithms for optical remote sensing images. Experimental results show that current advanced deep learning models achieve high accuracy on this dataset, demonstrating great potential for remote sensing detection in oil well sites.

The development and monitoring of Photovoltaic (PV) installations is of great interests for the Chinese energy management agency in recent years. The traditional land change detection of PV installations has issues pertaining to low efficiency and high missed detection rates. Therefore, this paper explores an efficient and high accurate detection method of PV installations land using changes from remote sensing images in order to help relevant stakeholders to better manage and monitor urban energy and environment. In this paper, Full Convolutional Network (FCN) and classical segmentation convolutional network (U-Net) based deep learning algorithms are used to build change detection models. To evaluate the model performance, we have built the change detection dataset from Northeast Petroleum University - Photovoltaic Remote Sensing Dataset (NEPU-PRSD) of PV installations in Western China. The experimental results show that both models can achieve good accuracy in change detection regarding PV installations.

Outcrop records contain very rich geological historical information, and the study of fractures in outcrop areas is an important part of geological exploration work. The accurate fracture information can provide useful technical support for the development and exploration of subsurface oil and gas. The outcrop images usually include unclear boundaries, complex structure and inconspicuous features, which make fracture detection from outcrop images a difficult task. To tackle these challenges, an improved U-Net algorithm based on the ResNeXt module is proposed in this paper to segment the fractures from the outcrop images. Experiments are conducted on the outcrop images from Yijianfang area in the Tarim Basin in China, and the results show that the proposed algorithm has improved the accuracy and IoU in fracture segmentation from the outcrop images.

In this paper, a unified virtual cloud storage method for federated data based on the middle layer is proposed, which aims at solving the problems of the heterogeneous data sources of remote sensing data in the shared platform among the federated remote sensing data management application under loose coupling mode. A federated remote sensing image data management model is developed based on NASA's Unified Metadata Model (UMM). This platform implements services such as unified access to multi-source heterogeneous image data which effectively solves the problem of heterogeneous data sources in the loosely coupled federated system, and provides better access methods for upper-level applications.

With the continuous growth of remote sensing data and the application of fast and effective atmosphere remote sensing inversion algorithm, this paper proposes a PM10 fast inversion approach based on Spark platform which uses Apache Spark as the analytics engine and integrates with the traditional atmospheric remote sensing inversion algorithm. We first store aerosol data which is MYD04_3K from NASA into HDFS. Then the inversion algorithm is combined with Spark via the function interface to realise rapid atmospheric remote sensing inversion. The experimental results based on Spark platform are compared with those obtained from the traditional physical hardware. The results prove that the proposed atmospheric remote sensing inversion method based on Spark has high efficiency.

The protection and monitoring of forest resources has drawn considerable national attention. Traditional deforestation monitoring requires a lot of manpower and material resources through manual visual interpretation and manual change patterns labelling, which has problems of low efficiency and high missed alarm rate. Therefore, this paper explores the detection for deforestation changes from remote sensing images based on deep learning framework, and aims to help forestry department manage and monitor forest resources. In this paper, an U-Net+LSTM framework is used to detect the changes of deforestation from remote sensing images. The evaluation data is Sentinel-2 dataset and the study area is Guangxi Sanjiang Dong Autonomous County in China. The results show that the F1 score of the framework is as high as 0.715, which proves the proposed model can effectively detect the change from forest to bare soil in remote sensing images.