Aerial Spatio-Temporal Image Generation via Latent Diffusion Model

SHANG Yuying; HOU Yingyan; LIU Zinan; LU Wanxuan; HUANG Yuhong; WANG Yixiao; YU Hongfeng; FU Kun

doi:10.11999/JEIT260165

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SHANG Yuying, HOU Yingyan, LIU Zinan, LU Wanxuan, HUANG Yuhong, WANG Yixiao, YU Hongfeng, FU Kun. Aerial Spatio-Temporal Image Generation via Latent Diffusion Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260165

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SHANG Yuying, HOU Yingyan, LIU Zinan, LU Wanxuan, HUANG Yuhong, WANG Yixiao, YU Hongfeng, FU Kun. Aerial Spatio-Temporal Image Generation via Latent Diffusion Model[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260165

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Aerial Spatio-Temporal Image Generation via Latent Diffusion Model

doi: 10.11999/JEIT260165 cstr: 32379.14.JEIT260165

SHANG Yuying^{1, 2, 3, 4},
HOU Yingyan^{1, 2, 3, 4
,
,},
LIU Zinan^{1, 2, 3},
LU Wanxuan^{1, 4},
HUANG Yuhong^{5, 1, 3, 4},
WANG Yixiao^{1, 2, 3, 4},
YU Hongfeng^{1, 4},
FU Kun^{1, 3, 4}

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
2.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, 100190, China
3.
University of Chinese Academy of Sciences, Beijing, 100190, China
4.
The Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China
5.
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences, Beijing, 100190, China

Received Date: 2026-02-06
Accepted Date: 2026-04-09
Rev Recd Date: 2026-03-09

Available Online: 2026-04-13

Abstract

Abstract

Objective Aerial Earth observation plays a pivotal role in environmental monitoring, disaster warning, and urban planning. However, constrained by flight platform endurance, mission window timeliness, and other operational limitations, the acquired aerial imagery often fails to fully characterize the long-term evolutionary processes of the Earth's surface. Although pre-trained diffusion models have demonstrated considerable potential in image generation, their application in the aerial domain remains challenging due to the scarcity of high-quality temporal annotation data and the semantic–visual misalignment arising from variable observation scales. To address these challenges, this paper proposes ASTIG, a training-free framework for Aerial Spatio-Temporal Image Generation. By leveraging the generative priors of pre-trained latent diffusion models and large language models, ASTIG establishes a novel paradigm for semantically controllable aerial temporal image generation. Methods ASTIG comprises three synergistically designed components. First, a dynamic semantic decomposition process is introduced to automatically parse complex aerial scene evolution descriptions into frame-level visual prompts, compensating for the lack of temporal semantic annotations in existing aerial image-text datasets. Second, a linguistic binding strategy is proposed to establish explicit associations between key ground objects and their corresponding visual attributes within the cross-attention mechanism of the diffusion model, thereby enhancing the semantic response precision of generated imagery. Third, a temporal anchor attention mechanism is integrated, which employs dual reference frames to enforce subject stability and background consistency across the generated temporal images, effectively suppressing inter-frame temporal drift under training-free conditions. Results and Discussions ASTIG and baseline models are evaluated on 7, 236 high-quality aerial spatio-temporal descriptions across six automated metrics, including subject consistency, background consistency, temporal flickering, motion smoothness, aesthetic quality, and imaging quality. Quantitative results (Tables 1 and 2) demonstrate the superiority of ASTIG in temporal image generation, with improvements of 3. 91% and 4. 57% in subject consistency and temporal smoothness over the frame-prompt baseline, respectively. Qualitative comparisons (Fig. 4) further highlight its robust capability in modeling long-term geospatial evolutionary imagery. Ablation studies validate the individual effectiveness of the linguistic binding strategy and the temporal anchor attention mechanism (Table 3 and Fig. 5). Sensitivity analyses of the intervention steps (Table 4 and Fig. 6) and binding strength (Table 5 and Fig. 7) provide systematic exploration of optimal parameter configurations. Furthermore, extensibility experiments under satellite perspectives (Figs. 8 and 9) reveal the potential of ASTIG to generalize beyond aerial platforms to broader Earth observation scenarios. Conclusions This paper proposes ASTIG, a training-free framework for aerial spatio-temporal image generation that addresses the scarcity of high-quality long-term temporal data and semantic–visual misalignment. By leveraging the generative priors of pre-trained latent diffusion models and large language models, ASTIG integrates dynamic semantic decomposition process, linguistic binding strategy, and temporal anchor attention mechanism to jointly address temporal semantic construction, semantic response precision, and inter-frame consistency. Experimental results demonstrate that ASTIG outperforms existing baseline methods across multiple automated evaluation metrics, offering a novel paradigm for aerial spatio-temporal image generation. As a training-free method, the performance of ASTIG is inherently bounded by the prior knowledge of the backbone model. Future work will explore geometric correction and nadir-view prior constraints to better align generated results with the physical properties of satellite imagery.
- Aerial spatio-temporal image,
- diffusion models,
- text-to-image generation,
- large language models,
- training-free generation

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References(35)

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