Automatic Caption Generation for Aerial Images: A Survey

Automatic creation of image descriptions, i.e. captioning of images, is an important topic in artificial intelligence (AI) that bridges the gap between computer vision (CV) and natural language processing (NLP). Currently, neural networks are becoming increasingly popular in captioning images and researchers are looking for more efficient models for CV and sequence-sequence systems. This study focuses on a new image caption generation model that is divided into two stages. Initially, low-level features, such as contrast, sharpness, color and their high-level counterparts, such as motion and facial impact score, are extracted. Then, an optimized convolutional neural network (CNN) is harnessed to generate the captions from images. To enhance the accuracy of the process, the weights of CNN are optimally tuned via spider monkey optimization with sine chaotic map evaluation (SMO-SCME). The development of the proposed method is evaluated with a diversity of metrics.

In recent years, with the rapid development of artificial intelligence, image caption has gradually attracted the attention of many researchers in the field of artificial intelligence and has become an interesting and arduous task. Image caption, automatically generating natural language descriptions according to the content observed in an image, is an important part of scene understanding, which combines the knowledge of computer vision and natural language processing. The application of image caption is extensive and significant, for example, the realization of human-computer interaction. This paper summarizes the related methods and focuses on the attention mechanism, which plays an important role in computer vision and is recently widely used in image caption generation tasks. Furthermore, the advantages and the shortcomings of these methods are discussed, providing the commonly used datasets and evaluation criteria in this field. Finally, this paper highlights some open challenges in the image caption task.

Image Captioning, the process of generating a textual description of an image, has emerged as a hot research due to its practical importance in many domains. It is a challenging task as it uses both Natural Language Processing and Computer Vision related fields to generate the captions. Despite the fact that the literature has reported notable image captioning methodologies, they still lag in accomplishing the substantial performance level for diverse datasets. This paper proposes an image caption generating mechanism based on Optimized Bidirectional Long Short-Term Memory (B-LSTM) model. We propose a variant of Moth Flame Optimization (PMFO), termed here as Proposed Moth Flame Optimization (PMFO), which has logarithmic spiral update based on correlation. The performance of the proposed model is demonstrated on benchmark datasets like Flicker 8 k, Flicker30k, VizWik and COCO datasets using renowned metrics such as CIDEr, BLEU, SPICE and ROUGH. The performance analysis proves that the B-LSTM achieves better performance on caption generation than state-of-the-art methods.

Geographic Artificial Intelligence (GeoAI) and Natural Language Processing (NLP) are two rapidly advancing technologies in spatial data analysis. GeoAI integrates artificial intelligence with geographic data to identify patterns, make predictions, and support decision-making. Meanwhile, NLP enables the processing and analysis of textual data related to spatial information, such as documents, reports, or geographic descriptions. The objective of this research is to obtain a representation of spatial data patterns through a series of processes, including problem identification, needs analysis, data collection and processing, document representation, and the application of Geographic Artificial Intelligence (GeoAI), Natural Language Processing (NLP), and Fuzzy Similarity methods to spatial or textual tax data and textual land data to identify data similarities. The research explores the integration of GeoAI and NLP in spatial data analysis to enhance the efficiency and accuracy of geographic data interpretation. The methods used in this study are based on artificial intelligence, which extracts spatial information from text and performs machine learning-based spatial analysis. The results demonstrate that the combination of GeoAI and NLP can improve the understanding of spatial patterns in unstructured data and support location-based decision-making processes. This research contributes to the development of more accurate spatial data analysis techniques that can be applied in various fields, such as urban planning, disaster management, and environmental analysis.

Climate change is increasingly disrupting businesses and ecosystems, creating urgent demand for data-driven adaptation strategies. This study examines how artificial intelligence (AI) can strengthen climate resilience across diverse industries, with particular attention to the innovative business strategies that help organizations respond to global environmental challenges. The objective is to address gaps in existing AI frameworks, focusing on developing countries with resource and technical limitations. The study highlights the significance of AI in fostering sustainable practices, particularly in climate change mitigation. A systematic review of 42 high-quality studies, published between 2010 and 2025 in the Scopus database, was carried out using the PRISMA framework. The analysis identifies key AI applications, technologies, and challenges. Data were organized according to industry applications, technological contributions, and obstacles. Key findings indicate that AI enhances climate risk assessment through predictive modelling, supports adaptive decision-making via scenario analysis, and optimizes resource allocation for sustainability. Applications in renewable energy, precision agriculture, and disaster management are also noted. However, significant barriers persist, including ethical concerns such as algorithmic bias and data privacy, technical complexities, and high financial costs. The review underscores the necessity of collaborative approaches, such as public-private partnerships, and the importance of conducive policy frameworks. The research's originality lies in its comprehensive synthesis of AI applications for climate resilience, offering actionable insights for scholars, practitioners, and policymakers. The findings highlight AI's potential to drive sustainable business models while calling for interdisciplinary research to address scalability and ethical implications in resource-constrained environments.

Image caption generation is a fundamental task to build a bridge between image and its description in text, which is drawing increasing interest in artificial intelligence. Images and textual sentences are viewed as two different carriers of information, which are symmetric and unified in the same content of visual scene. The existing image captioning methods rarely consider generating a final description sentence in a coarse-grained to fine-grained way, which is how humans understand the surrounding scenes; and the generated sentence sometimes only describes coarse-grained image content. Therefore, we propose a coarse-to-fine-grained hierarchical generation method for image captioning, named SDA-CFGHG, to address the two problems above. The core of our SDA-CFGHG method is a sequential dual attention that is used to fuse different grained visual information with sequential means. The advantage of our SDA-CFGHG method is that it can achieve image captioning in a coarse-to-fine-grained way and the generated textual sentence can capture details of the raw image to some degree. Moreover, we validate the impressive performance of our method on benchmark datasets—MS COCO, Flickr—with several popular evaluation metrics—CIDEr, SPICE, METEOR, ROUGE-L, and BLEU.

Image Captioning is an emergent topic of research in the domain of artificial intelligence (AI). It utilizes an integration of Computer Vision (CV) and Natural Language Processing (NLP) for generating the image descriptions. It finds use in several application areas namely recommendation in editing applications, utilization in virtual assistance, etc. The development of NLP and deep learning (DL) models find useful to derive a bridge among the visual details and textual semantics. In this view, this paper introduces an Oppositional Harris Hawks Optimization with Deep Learning based Image Captioning (OHHO-DLIC) technique. The OHHO-DLIC technique involves the design of distinct levels of pre-processing. Moreover, the feature extraction of the images is carried out by the use of EfficientNet model. Furthermore, the image captioning is performed by bidirectional long short term memory (BiLSTM) model, comprising encoder as well as decoder. At last, the oppositional Harris Hawks optimization (OHHO) based hyperparameter tuning process is performed for effectively adjusting the hyperparameter of the EfficientNet and BiLSTM models. The experimental analysis of the OHHO-DLIC technique is carried out on the Flickr 8k Dataset and a comprehensive comparative analysis highlighted the better performance over the recent approaches.

A survey on automatic image caption generation

Image captioning refers to automatic generation of descriptive texts according to the visual content of images. It is a technique integrating multiple disciplines including the computer vision (CV), natural language processing (NLP) and artificial intelligence. In recent years, substantial research efforts have been devoted to generate image caption with impressive progress. To summarize the recent advances in image captioning, we present a comprehensive review on image captioning, covering both traditional methods and recent deep learning-based techniques. Specifically, we first briefly review the early traditional works based on the retrieval and template. Then deep learning-based image captioning researches are focused, which is categorized into the encoder-decoder framework, attention mechanism and training strategies on the basis of model structures and training manners for a detailed introduction. After that, we summarize the publicly available datasets, evaluation metrics and those proposed for specific requirements, and then compare the state of the art methods on the MS COCO dataset. Finally, we provide some discussions on open challenges and future research directions.

Enhancing Cross-Linguistic Image Caption Generation with Indian Multilingual Voice Interfaces using Deep Learning Techniques

Abstract. Artificial Intelligence (AI) has the potential to play a significant role in disaster risk management – from predicting disasters to optimizing response efforts. However, its application in disaster management also presents significant challenges. These challenges include the need for high-quality and diverse data, compatibility with existing systems and technologies, ethical and social implications, and ongoing research and development. In addition, data privacy and security is a critical issue as the use of AI in disaster management often involves the collection and analysis of sensitive information. Addressing these challenges is crucial to ensuring that AI is developed and used in ways that are fair, equitable, and effective in reducing the impact of disasters. The aim of the paper is to provide an analysis of challenges of artificial intelligence application for disaster risk management.

This article presents a novel conversational artificial intelligence (CAI)-enabled active ideation system as a creative idea generation tool to assist novice product designers in mitigating the initial latency and ideation bottlenecks that are commonly observed. It is a dynamic, interactive, and contextually responsive approach, actively involving a large language model (LLM) from the domain of natural language processing (NLP) in artificial intelligence (AI) to produce multiple statements of potential ideas for different design problems. Integrating such AI models with ideation creates what we refer to as an active ideation scenario, which helps foster continuous dialog-based interaction, context-sensitive conversation, and prolific idea generation. An empirical study was conducted with 30 novice product designers to generate multiple ideas for given problems using traditional methods and the new CAI-based interface. The ideas generated by both methods were qualitatively evaluated by a panel of experts. The findings demonstrated the relative superiority of the proposed tool for generating prolific, meaningful, novel, and diverse ideas. The interface was enhanced by incorporating a prompt-engineered structured dialog style for each ideation stage to make it uniform and more convenient for the product designers. A pilot study was conducted and the resulting responses of such a structured CAI interface were found to be more succinct and aligned toward the subsequent design stage. The article thus established the rich potential of using generative AI (Gen-AI) for the early ill-structured phase of the creative product design process.

Artificial intelligence (AI)-based emergency response systems have emerged as critical enablers of smart infrastructure safety, offering enhanced real-time decision-making, risk assessment, and disaster mitigation strategies across various domains. This systematic literature review, encompassing 424 eligible studies, investigates the integration of machine learning (ML), deep learning (DL), computer vision, IoT-enabled predictive analytics, and AI-powered robotics in optimizing emergency response mechanisms. The study comprehensively examines AI applications in disaster management, real-time incident detection, healthcare emergency response, industrial hazard prevention, cybersecurity frameworks, and intelligent traffic control, providing a detailed assessment of technological advancements and challenges in AI adoption. The findings reveal that AI has significantly improved predictive accuracy, automated hazard detection, and emergency resource optimization, leading to faster response times, minimized human error, and enhanced situational awareness in crisis management. AI-driven predictive analytics models have enabled early warning systems for earthquakes, floods, and wildfires, facilitating proactive disaster preparedness and risk mitigation. In real-time emergency response, AI-powered computer vision and sensor-based surveillance technologies have improved incident detection, reducing intervention delays and ensuring more efficient allocation of emergency resources. In the healthcare sector, AI-enhanced diagnostic tools, triage automation, and geospatial analytics for ambulance dispatch have streamlined medical crisis management, improving survival rates and reducing treatment delays. Additionally, AI-integrated industrial safety frameworks, robotic automation, and cybersecurity intelligence systems have strengthened workplace hazard prevention, cyber threat detection, and emergency communication resilience, ensuring safer and more secure operational environments. Despite these advancements, several challenges related to interoperability, regulatory constraints, cybersecurity vulnerabilities, algorithmic biases, and ethical concerns persist, hindering large-scale AI adoption in emergency response systems. This review provides a comprehensive synthesis of AI’s transformative role in modern emergency management, offering insights into technological developments, limitations, and policy considerations necessary to enhance AI-driven crisis response strategies and ensure more effective, scalable, and resilient emergency safety infrastructures worldwide.

Image caption generation becomes a raising topic in computer vision and artificial intelligence. In order to solve the problem of stiff description, we intend to extract richer features using convolutional neural network (CNN). A neural and probabilistic framework has been proposed consequently which combines CNN with a special form of recurrent neural network (RNN) to produce an end-to-end image captioning. We use a model that takes advantage of word to vector to encode the variable length input into a fixed dimensional vector. Considering the description of the object in an image is not specific enough, we introduce an attention mechanism through visualization to show how the model is able to fix its gaze on salient objects. We validate our model on three benchmark datasets and get great performance by using standard evaluation metrics.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.