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In today’s global society, climate change has become a focal point of concern (Waheed, et al., 2019). Greenhouse gas emissions and rising atmospheric temperatures are triggering various extreme weather events, rising of sea levels, and disruption of ecological balance. These changes not only affect the stability and sustainable development of human society but also pose a severe threat to the Earth’s ecosystems and biodiversity. Faced with this global challenge, finding effective solutions has become urgent (R. Li, et al., 2021). Carbon neutrality is not only an environmental concept but also a commitment to action, encompassing various sectors and levels. From energy production to industrial manufacturing, transportation to agricultural production, and individual lifestyles to business operations, carbon neutrality is gradually shaping our way of life and economic models. By implementing a series of measures such as reducing greenhouse gas emissions, improving energy efficiency, promoting renewable energy, and supporting carbon offset projects, we can gradually achieve a reduction in global carbon emissions and an increase in carbon absorption, ultimately attaining carbon balance. In recent years, deep learning, as a pivotal technology in the field of artificial intelligence, has experienced significant breakthroughs and found applications across various domains. In the environmental sector, the application of deep learning is gradually demonstrating its immense potential, providing new perspectives and methods for addressing environmental issues (X. Liu et al., 2023).
Visual question answering (VQA) systems combine computer vision and natural language processing, aiming to enable computers to understand images and answer questions related to these images. In the context of environmental conservation research, VQA systems can provide us with deeper insights and more accurate data analysis and decision support (Akula et al., 2021). Specifically, VQA systems can be used for environmental monitoring and assessment. By analyzing images and questions, VQA systems can identify environmental features in the images and key points of the questions, assisting researchers in quantitatively assessing environmental conditions. For example, VQA systems can be used to analyze satellite images to detect the green coverage in urban areas, thereby assessing changes in the urban ecological environment (Anderson et al., 2018). VQA systems also contribute to enhancing environmental awareness and education. By asking questions about environmental protection to VQA systems, individuals can gain a deeper understanding of the nature and impact of environmental issues. Furthermore, applying VQA systems to environmental education can help the public better grasp the importance of environmental protection, promoting an increase in environmental awareness. Additionally, VQA systems can also play a role in environmental decision-making and planning. In the context of environmental conservation research for the Olympic Games, VQA systems can be used to analyze the environmental impact during the venue construction process, providing information on environmental conservation measures to decision-makers (Liu et al., 2021). Furthermore, by training VQA systems and analyzing data, it is possible to predict the effectiveness of various environmental protection strategies, providing scientific support for decision-making (Antol et al., 2015).
Below are common research methods:
Vision-and-language bidirectional encoder representations from transformers (ViLBERT) is an advanced multimodal pre-training model designed to deepen the integration of computer vision and natural language processing fields (Lu et al., 2019a). ViLBERT seamlessly integrates image and text information within a unified framework, delivering robust performance and capabilities for multimodal tasks, including VQA. Its design ingeniously incorporates the successful principles of the BERT model, extending them into the realm of images. This equips ViLBERT with the unique ability to adapt to both natural language and visual data. Through pre-training, it captures the multimodal semantic information of text and images and maps them into a shared embedding space. Despite its excellence in visual and language tasks, ViLBERT’s design structure is complex, incorporating multiple layers of attention mechanisms and a significant number of parameters. This complexity results in a demand for substantial computational resources and time during training and inference, limiting its practical application range (Ke et al., 2023).