TAMMs: Change Understanding and Forecasting in Satellite Image Time Series with Temporal-Aware Multimodal Models
Authors: Zhongbin Guo
Yuhao Wang
Ping Jian
Chengzhi Li
Xinyue Chen
Zhen Yang
Ertai E
Venue: ICLR 2026 Poster (2026)
Keywords: Remote Sensing
Satellite Image Time Series
Temporal Reasoning
Generative models
Change-aware Generation
Multimodal Large Language Models
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We introduce TAMMs, a unified framework which provides a single solution to both describe historical changes and forecast future scenes, significantly outperforming prior methods on both tasks.

Abstract: Temporal Change Description (TCD) and Future Satellite Image Forecasting (FSIF) are critical, yet historically disjointed tasks in Satellite Image Time Series (SITS) analysis. Both are fundamentally limited by the common challenge of modeling long-range temporal dynamics. To explore how to improve the performance of methods on both tasks simultaneously by enhancing long-range temporal understanding capabilities, we introduce **TAMMs**, the first unified framework designed to jointly perform TCD and FSIF within a single MLLM-diffusion architecture. TAMMs introduces two key innovations: Temporal Adaptation Modules (**TAM**) enhance frozen MLLM's ability to comprehend long-range dynamics, and Semantic-Fused Control Injection (**SFCI**) mechanism translates this change understanding into fine-grained generative control. This synergistic design makes the understanding from the TCD task to directly inform and improve the consistency of the FSIF task. Extensive experiments demonstrate TAMMs significantly outperforms state-of-the-art specialist baselines on both tasks. Our dataset can be found at https://huggingface.co/datasets/IceInPot/TAMMs .

Published as a conference paper at ICLR 2026
TAMMS: CHANGE UNDERSTANDING AND FORE- CASTING IN SATELLITE IMAGE TIME SERIES WITH TEMPORAL-A A WARE MULTIMODAL MODELS
Zhongbin Guo', Yuhao Wang', Ping Jian': Chengzhi Li', Xinyue Chen', Zhen Yang', Ertai E² School of Computer Science & Technology. Beijing Institute of Technology
School of Computing, National University of Singapore
buozhongbinebit.eddu .dd..c.t "jianßbit.edu.
ABSTRACT
Temporal Change Description (TCD) and Future Satellite Image Forecasting (FSIF) are critical, yet historically disjointed tasks in Satellite Image Time Se- ries (SITS) analysis. Both are fundamentally limited by the common challenge of modeling long-range temporal dynamics. To explore how to improve the perfor- mance of methods on both tasks simultaneously by enhancing long-range tempo- ral understanding capabilities, we introduce TAMMs, the first unified framework designed to jointly perform TCD and FSIF within a single MLLM-ddiffsiion archi- tecture. TAMMs introduces two key innovations: Temporal Adaptation Modules (TAM) enhance frozen MI.LM's ability to comprehend long-range dynamics, and Susedd Control Injection (SFCI) mechanism translates this change derstanding into fine-grained generative control. This synergistic design makes the understanding from the TCD task to directly inform and improve the consis- tency of the FSIF task. Extensive experiments demonstrate TAMMs significantly outperforms state-of of-the-art specialist baselines on both tasks.
INTRODUCTION
Understanding temporal change and forecasting future scenes from Satellite Image Time Series (SITS) is fundamental to critical applications et al. [Lraa) a profound challenge: predicting the future requires a deep understanding of the past change. While foundation models like Multimodal Large Language Models (MLLMs) Wu et al. (2024): Bai et al. (025) and diffusion- based generators Sastry et al. (2024); Liu et al. (2025a) have demonstrated remarkable capabilities. their application to this domain remains fragmented and fundamentally limited. As illustrated in Fig. [T] the field is split into disjointed paradigms: static image captioning that ignores temporal context Bazi et al. (2024), and temporal description methods that lack semantic depth of MLLMs Peng et al. (2024); similarly, generative approaches divided between single- image synthesis that cannot model evolution Liu et al. (2025a), and temporal forecasting struggles with long-term consistency because they fail to grasp semantic drivers of change Khanna et al. 2024). This division reflects a scientific gap- - the absence of a unified framework of temporal- aware multimodal model for satellite image change understanding and forecasting. To address this, We present TAMMs which can reason about why changes occur and use that reasoning to generate what will happen next.
al. (2024), yet poses
How can model reasons about why these temporal changes occur? Existing Temporal Change Description methods fuse temporal multi-graph information through simple interactions, and their long-range temporal reasoning capabilities are limited |eng et al. (2024). To bridge this gap, we introduce novel, parameter-efficieet approach to awaken the latent temporal reasoning abilities of frozen MLLM through Temporal Adaptation Modules (TAM), designed to both inject explicit awareness of arbitrary time intervals and guide the model's focus onto salient change narratives, enhancing its performance in long-range temporal change reasoning.
How can model use temporal change reasoning to generate what will happen next? As existing methods use only text guidance and lack fine-grained temporal semantic interaction Khanna et al.
"Corresponding Author.

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Published as a conference paper at ICLR 2026
introduce novel mechanism to bridge this understanding with generation: Semantic- uusdd Control Injection (SFCI, which converts MLLM's abstract, cemporally-groumee grounded semantic features into precise, multi-scale control signals. It adaptively fuses these semantic signals with low-level structural priors using trainable gated fusion units, directly guiding the diffusion model's denoising process, which ensures forecasted images not just plausible standalone pictures, but temporally consistent continuations of historical trajectory observed.
2024
This synergistic coupling of temporal- aware MLLM (via TAM) and consistency- aware diffusion model (via SFCI) forms our unified framework, which we name TAMMs. The framework designed produce two synergistic outputs: high- quality temporal change description from the MLLM, and temporally consistent fu- ture image forecast from the guided diffu- sion model.
(Disjeinted Captioning Gereration)
Single Trage Caphiening
Sing's Trage
CRIN Modil)
oefaror Vodka
M
Ceneric Seapy Greee
X Tempard Context
X Temperal Enolution
(cToorpard Change Desorigtion MNom MILM)
(o) Temporrd Irrage Generatisn (Iscus5teer)
Mocoll
Birasion Model
X Lnmited Semantic Depth
X Loold form Condsfercy
Closedi-Loop Framework
While both tasks can be assessed with standard metries, rigorously evaluating the temporal consistency forecast is a sig- nificant challenge, existing metries suf- fer from an evaluation gap in this re- gard. To address this, we introduce the Temporal Consistency Score (TCS), the first metric designed specifically quan- tify the consistency of predicted changes against historical dynamics. Extensive experiments demonstrate TAMMs signif- icantly outperforms all baselines on both tasks, with its superiority in forecasting particularly highlighted by our TCS met- ric.
Tenpardl
wall Tempard Soundia Seneric bate
Change
Searra srane
Coratrtency Awcre Diffuaton Medel
Fitas Farmuctin
Figure 1: Our TAMMs framework unifies temporal change understanding and future forecasting, using temporal-aware MLLM to guide a diffusion model in synergistic process.
Our main contributions are summarized as follows:
We propose TAMMs, the first unified framework to synergistically couple temporal change un- derstanding with future forecasting in mutually reinforcing manner.
We introduce TAM awaken latent long-range temporal reasoning abilities of MILMM for SITS.
We design novel SFCI mechanism to translate the MLLM's high-level temporal understanding into fine-grained generative control.
We design the TCS, the first metric designed to specifically quantify the consistency of predicted changes against historical dynamics.
2 TASK DEFINITION
Our work introduces unified framework to address two coupled, yet historically disjointed, tasks central to SITS analysis. We argue that solving them synergistically is key to unlocking more reli- able, reasoning- baasdd forecasting.
2.1 TASK 1: TEMPORAL CHANGE UNDERSTANDING
Given sequence of temporally ordered satellite images, I I2....,,II the goal to generate natural language description, D. that accurately summarizes the key dynamic changes that have occurred over the observation period. This requires comprehending the temporal evolution, such urban expansion agricultural cycles. While prior work like SITSCC Peng et al. (0244) has explored this, it often lacks the deep semantic reasoning of modern MLLMs, struggling to capture the underlying causal narrative of the observed changes.
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Strutiunn Slade
Coni
Fudura Tmaae Predictin
2001-2-28
TimeStarip Potches
Temporal Excoding
Diltsion
Sabi
Pretrained MLLM (lodgy Boody)
Semandis Preossor
2005-10 10-22
2008 7
Scane Description The inages show an agroouluuaal
Change Description derial photos cyclical monsgnreent ponds and Rolds, consitent agricultural actiuity, and infrastracture expensions restiontial
Text ordedding
and Reeids, small village, infrastracture. The ponds appear
lecusl
CTP
Tirte-series Feotures
Faalures
Text Festures
Forward
Figure 2: The TAMMs framework architecture. The temporal change understanding stage uses Temporal Adaptation Modules (TAM) to awaken the temporal reasoning of frozen MLLM. The future forecasting stage then guides frozen diffusion U-Net using core Remantic-Fuseed Con- trol Injection (SFCI) mechanism, which translates the MLLM's deep temporal understanding (M,) into multi-scale control signals. Critically, only the lightweight adapter components trained.
2.2 TASK 2: TEMPORALLY CONSISTENT FUTURE FORECASTING
Given the same historical sequence I, the goal to generate a plausible future image î for a subsequent time point. A critical and often overlooked requirement is that the forecast must be temporally consistent: the generated changes should represent a logical continuation of the trends and dynamics observed in the historical data. Approaches like DiffusionSat Khanna al. (2024) primarily conditioned on metadata can only exhibit some differences in seasonal evolution lack mechanisms ensure that the generated future explicitly consistent with the nuanced, patch-level evolution observed in the past.
2.3 EVALUATION METRIC FOR TEMPORAL CONSISTENCY
A significant challenge in temporal forecasting is that standard image quality metrics (e.g., PSNR Zhang et al. (2020), SSIM Wang al. (2004)) are insufficient, as they possess an evaluation gap: they cannot penalize temporally implausible futures. A prediction could be perceptually realistic but completely inconsistent with historical trends. address this, we introduce a new metric.
Temporal Consistency Score (TCS). This metric quantifies the temporal plausibility of forecast by comparing the spatio-temporal dynamies of predicted changes against those the recent past. Let C(I,, I)) be change detection function producing binary change mask. We define the histor- ical change mask as MAist C(I-1. and the predicted change mask Mpred C(I, Í411), using the P2V-CD framework Lin et al. (003)). The TCS is then formulated as the product of two scores assessing geometric consistency.
Spatial Proximity Score (SPS) quantifies the locational concordance between the change centroids:
SPS(M Mprea) = exp [lI(M)rea)) p((Masst) 1|
(1)
where (a-) is the centroid operator and 0.2 is scaling parameter. Area Consistency Score (ACS) evaluates the congruence the magnitude of change:
9. || M
ACS(M Mpred) = exp (
(2)
max M ,redl,
(Mpred), M +
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where | is the area (pixel count) operator and / 1.0.
The final composite score is the product of these two components:
TCS SPS ACS
(3)
By anchoring the evaluation in the spatial distribution and magnitude of detected transformations, TCS provides robust and discriminative measure of temporal consistency.
3 METHODOLOGY
As outlined in the introduction, our TAMMs framework is designed to first empower an MLLM understand temporal dynamics, and then use this understanding to guide a generative model. This section details the core technical innovations for each part: the Temporal Adaptation Modules (TAM) for the MLLM, and the Semantic-Fuse Fused Control Injection (SFCT) for the diffusion model, which's framework can be overviewed in Figure 2
3.1 AWAKENING TEMPORAL CHANGE UNDERSTANDING IN MLLMS
key challenge is that pretrained MLLMs lack an inherent understanding of the sparse, long-term, and irregular time intervals found in SITS. However, offte-of-the-art video-centric MLLMs Bai et al. (2025); Liu et al. (2025b) are typically optimized for densely sampled video data with fixed, short-term temporal intervals. Satellite Image Time Series, in contrast, where the intervals between consecutive images can span several years. This fundamental mismatch in temporal structure ren- ders existing models incapable of accurately perceiving the gradual, long-term evolution inherent in SITS. To address this without costly full-model finetuning, we introduce Temporal Adaptation Mod- ules (TAM), which has two complementary, parameter-efficient modules designed to "reprogram" frozen MILMM for temporal awareness, as illustrated in Fig. 3
Physical Time Encoder (PTE). To make the passage of time explicit the MILM, PTE injects a learnable temporal token [TIME DIFF] into the input sequence. This token's 's embedding t; is dynamically conditioned on the specific time interval Al, via a small MLP, allowing represent arbitrary time gaps:
(4) These temporal tokens are then inserted between the visual features of consecutive images (V,, I),, allowing the MLLM's attention mechanism directly associate visual changes with the corresponding time interval.
t, = eITIMEDDIFF] + MLPune(((AAt)
Contextual Temporal Prompting (CTP), While PTE provides the raw temporal signal, CTP pro- vides high-level semantic guidance. CTP provides detailed scene description, as well using structured textual prompt that instructs the MLLM to perform specific temporal reasoning task: describing the changes observed in the visual sequence. This focuses MLLM's powerful but general- purpose reasoning abilities on the exact task of identifying and articulating temporal dynamics. Concrete prompt structure can be found in Appendix A.4
Synergistic Output. Together, PTE and CTP enable the MILMM to produce two outputs: a textual description and, more critically, semantic feature vector M, This vector encapsulates the MILL''s temporal understanding and serves as the primary conditioning signal for the generative module. A composite loss, L Cret CeeppCemmp guides the training of the adapters by balancing text accuracy with temporal regularization.
3.2 SEMANTIC-FUSED CONTROL INJECTION (SFCI) FOR TEMPORAL FORECASTING
Having awakened the MILM's temporal reasoning to produce a rich semantic feature vector M,, the central challenge becomes translating this abstract change understanding into fine-grained control for a diffusion-based generator. Previous approach like standard ControlNet ZZang et al. (2023) use this understanding to generate textual description to control the procedure of generation. However, this is fundamentally insufficient for high-consistency forecasting. A global text prompt, even a detailed one, is too coarse signal to provide the necessary letch-level semantic guidance for how specific regions should evolve into the next time step, which cannot ensure the high-consistency generation of fine-grained image details required by temporal forecasting.
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Ted
Text
Pre-trained (Body)
Lint head
X1
Loss
Text
Fusion Embessiigg
Output Patch Emboeddings y
Cross-entropy
Multi-Hoon Head Attention
Add Layer Nornt
Flatten & Linear
Temporal
Feed Forward
Ap
Change Descrrption
Adai & Layer Norm
Between the first and second imagess, building appears in the bottove-left corner. the third image, this building still present. but there's additional, structure on the nearby island.
Output Embeddings
TimeStamp Patches
Temporal
[TIME_ DIFF)
Encoding
tokon
Figure 3: Temporal enhancement modules for the MLLM. PTE processes timestamps into learnable tokens to inject explicit temporal awareness into the visual feature stream. CTP (via prompting) provides high-level task guidance. Together, they enable the frozen MLLM to comprehend change narratives, which are then optimized via a composite loss.
To overcome this limitation, we introduce Semantic-Fused Control Injection (SFCI) mechanism, which is designed to bypass the bottleneck coarse textual control. Implemented via an Enhanced Control Module (ECM) that works in parallel with frozen diffusion U-Net Rombach et al. (2022), SFCT directly translates the MLLM's rich, multi-imaae temporal change understanding encapsu- lated in M, into multi-scale, spatially-aware control signals. As shown Fig. 4 it synergistically fuses this high-level semantic guidance with low-level structural priors, enabling precise, patch-level control that ensures the generated future is temporally consistent evolution of the past.
The SFCI Mechanism. For each feature map h (ene) from the I-th level the U-Net encoder, SFCI extracts and adaptively fuses structural and semantic information.
(1) Structural Path: To capture the low-level spatio-temporal evolution, the encoder features first processed by a frozen 3D Control Block Khanna et al. 2024), yielding a structural control signal h(oorl) that encodes underlying visual dynamics:
h (ctrl) CtrlBlock (1) (enc) E ER Xx
(2) Semantic Path:
The MLLM's semantic feature M, is projected into non-spatial intermediate representation sy (pros) by level-specific processor (o,, This then tiled create the spatially-aware guidance signal sy:
(prof) SemProe (M.), S TH, (p(oo))
s
(3) Adaptive Gated Fusion: A dynamic gate gi, computed from the intermediate semantic features (pros)
St adaptively interpolates between the structural signal h (otrl) and the semantic guidance St to produce the fused features f;:
gi = =T(H, Wi) (o (((
(ctrl)
f = (1 gi)
St
where T(H,, Wi} denotes a tiling operation that replicates its input tensor across the spatial dimensions Wi) to match the feature map size.
(4) Temporal Refinement: To model long-range dependencies, the fused features are refined by Temporal Transformer T)mpTrans As operator V first reshapes the tensor to treat the time dimension as the sequence length for the transformer. The output is then integrated via a weighted residual connection:
2 aj 1 (TempTranss") (Ii(f)))
(F)(()) + (1 -a)) fr
where ay E [0, is a learned mixing parameter and ; is its inverse operation.
Integration with U-Net Decoder. The final, refined control signal z from the ECM at each level is aggregated along the time dimension produce 2/. This unified, multi-scale control signal is then
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Diffusion U-Net with SFCI
SFCI Module I-th Block
axdition
Mt
ME
Processor
VAE
Impul Come
Denoised Latent
Transfavner
Temporal.
Gated Fusion
Encoder
CLIP Text
3D Control
Change
k
MUUM
Description
Text
G0005
Figure 4: The Semantic-Fusse Control Injection (SFCI) mechanism. SFCI guides the diffusion U- Net by dynamically fusing signals from two parallel streams: a Structural Path (green) that extracts low-level spatio-temporal dynamics, and Semantic Path (gray) that translates the MLLM's high- level temporal understanding (M,). The resulting fused, multi-scale control signal is injected into the U-Net decoder ensure temporally consistent forecasting.
injected into the corresponding U-Net decoder layer by adding it to the skip connection features, which allows TAMMs to modulate the generation process at multiple spatial scales, ensuring the final forecast image aligns with both the low-level structural evolution and the high-level semantic changes derived from the MILM.
Staged Training Strategy. We employ a two-stage training strategy to stably integrate semantic guidance. First, train only the structural path to learn foundational spatio-temporal priors from the image data. Then, with the structural path frozen, we train the semantic components to translate the MILM's guidance into control signals, which prevents catastrophic forgetting. Both stages opti- mize a standard diffusion loss. A detailed breakdown of the training protocol and loss formulations provided in Appendix A.6
EXPERIMENTS
4.1 EXPERIMENTAL SETUP
4.1.1 DATASETS
Our experiments leverage large-scale dataset curated for temporal analysis. The training set con- sists of 37,003 multi-temporal image sequences from the fMoW dataset Christie al. (2018), with descriptive labels automatically generated by Qwen2.5-VI. model to enable training at scale. For rigorous and unbiased evaluation, we curated an independent testset of 150 challenging, long-term (5-10 years) sequences from Google Earth. The ground-truth change descriptions for testset were annotated by human experts, providing high-quality benchmark for evaluation. Further details on data curation process, annotation prompts, and data distribution are provided in Appendix[A.1
4.1.2 EVALUATION METRICS.
We evaluate our model's performance on two tasks. For assessing the textual descriptions summa- rizing temporal changes, utilize standard natural language generation metries: BLEU-4 Papineni
ct al. (200), METEOR Lavie & Agarwal (2007). ROUGE-L Lin (2004), and CIDEr-D Vedantam al. 2015). which collectively measure aspects like a-gram precision, word alignment considering synonyms, longest common subsequence overlap, and consensus among reference descriptions. To evaluate predicted images, besides standard metrics PSNR ZZhang al. (2020), SSIM [Wang et al. 2004), and LPIPS /Zhang et al. (2018), we further introduce TCS metric for evaluating temporal image generation, which has detailed in Section 2.3
4.1.3 IMPLEMENTATION DETAILS
Our framework is conducted on two NVIDIA A6000 GPUs. The MLLM backbone is a frozen PrepSeck-VL2NN al. (2024) due to its MoE architecture and open-source accessibility, while the generative component adapts the U-Net architecture from DiffusionSatl Khanna et al. (2024) which based Stable Diffusion 2-1. We initially considered native unified models (Chameleon Team

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Temporal Change Description
Future Satellite Image Forecasting
Model
B-4f
M
R-L
C-D1
Pr
St
L4p
TCSt
Baselines for Temporal Change Description
Cormeer Liu ct al.
2022
0,1285
0.1930
0.3489
0.5344
SITSCC Peng of al. 2024
0.2122
0.2961
0.4701
0.6244
Owen2.5-VII-11
2025
0.2089
0.2845
0.4612
0.7156
DeepSeck-VL2 Wu ct al.
2024
0.1920
0.2382
0.4603
0.7362
TEOChat Irvin et al.
2025
0.2398
0.3102
0.4735
0.8267
Baselines for Future Satellite Image Forecasting Geosynth-Camm Sastry et al. (2024
11,3752
0.1757
0.7559
0.2170
MCVD Voleti el al 2022
9,2208
0.2098
0.4970
0.1930
DiffusionSat Khanna
2024
11.8878
0.1520
0.5225
0.7624
Our Unified Model
TAMMs (Ours)
0.2669
0.3312
0.4690
0.9030
12.0697
0.1831
0.4931
0.9690
Table I: Comprehensive evaluation on Temporal Change Description and Future Satellite Image Forecasting tasks. Metrics are abbreviated: B-4 (BLEU-4), M (METEOR), R-L (ROUGE-L), C-D (CIDEr-D), P (PSNR), (SSIM), L (LPIPS). Models are grouped by their primary task. A dash (-) indicates the metric not applicable for that model. Bold is best; underline second best.
2004), Vanus-Pro Chen al. (2255) which could thcoretically leverage cross-task information more effectively, but preliminary experiments showed significant performance gaps compared to specialized models, making them unsuitable for demanding remote sensing tasks.
A critical component of our methodology is a multi-stage training strategy for the generative module. This approach is designed to first establish robust spatio-temporal structural control before integrat- ing the MILM's high-level semantic guidance. This decoupling prevents catastrophic forgetting and ensures stable fusion of the low-level structural priors and high-level semantic reasoning. A detailed breakdown of our three-stage training protocol, including specific learning rates and opti- mizer schedules for each stage, provided in Appendix AA992 Comprehensive architectural details and full list of hyperparameters can also be found in Table
4.2
METHOD COMPARISON
4.2.1
BASELINES
We evaluate TAMMs against comprehensive set of state-of-the-art methods for two primary tasks.
For Temporal Change Description, we compare against three categories of models: (1) Special- ized remote sensing change captioning models, including the bi-temporal RSICC-Former Liu et al.
(022) and the multi- multi-tteppoal SITSCC[Peng et al. (2024). (2) General- purpose MLLMs, includ- ing Qwen2.55VL-2
Bai et al. (2055) and DeepSeek- VL2 Wu al. (2024), evaluated in finetuned settings on same datasets as TAMMs to assess their native temporal reasoning abilities. (3) A spe- cialized temporal MILM, TEOChat Irvin (2025), designed for earth observation conversations.
For Future Satellite Image Prediction, our baselines represent three distinct conditioning strate- gies: (1) Methods relying single-fram frame structural guidance, represented by GeoSynth-Cand [Sas try et al. (2024), which uses edge maps from only the last observed image. (2) Approaches adapted from video prediction, represented by MCVD MCvt/Voleti al. (202)), a conditional diffusion model. (3) Models using metadata conditioning, represented by DiffusionSat Khanna et al. (2024), generative foundation model for satellite imagery.
4.2.2 QUANTITATIVE RESULTS
Table shows that TAMMs demonstrates superior resultscompared to all evaluated baselines.
In temporal change description, TAMMs outperforms prior methods such SITSCC (0.2669 vs. 0.2122 BLEU-4, 0.3312 vs. 0.2961 METEOR) and DeepSeek- VL2 (0.9030 vs. 0.7362 CIDEr- D), highlighting its enhanced temporal reasoning and generation fidelity. For future satellite image prediction, TAMMs achieves the highest PSNR (12.07), competitive SSIM 1831 vs. 0.2098 for MCVD), and the best LPIPS (0.4931).
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RSICClormer: Sume houses buik along the road.
SITS.CC Pictures have not changed.
DeepSeekkVILa V122: buildings greenery indicating ungroved visbility comparred previous images.
TAMMs: The parking loi in the boctom-left corner has grown larger. The tree surrounding the terminal have
(b)
incrossed density. huilding visible top-ight of image.
along the SITS SITS.CC: CC: Some buildings are built on the burclan,, many green trees ure around curved river.
RSICCloemer:
DeepSeek-V.2:: open field adjucent
purking appear near swimming pool. TAMMs: A more developed camplex of buildings in the lower right corner of the image sequence,
(a)
(b)
indicating possible human development.
RSICCformer: Roads built top, built
SITS CC: More house appears in the images.
DeegStok-V1.2
buildings within village including distinct nod roof, TAMMs: small rad-rooieed strature near the conter-iigtta large blue-mated building oppear along the road,
(a)
(b)
(c)
building
increase.
Figure 5: Qualitative comparison temporal change description methods. Sequential images (a-c) shown for each example, followed by outputs from baseline models and TAMMs. It can be that TAMMs has noticed many change details that other models have not paid attention to.
We notice the improvements in standard quality metrics (SSIM) modest, this pattern is consistent with recent diffusion-based forecasting literature, where models that better preserve high-frequene and structural details often trade small amount of PSNR/SSIM for more realistic and temporally consistent predictions Khanna et al. (2024). In our setting, such perceptual-tempooal fidelity (cap- tured by LPIPS and especially TCS) is more critical than maximizing distortion-basee scores, be- cause downstream change analysis depends on plausible evolution rather than exact pixel-wise re- construction. TAMMs demonstrates substantial advantage in TCS, achieving 0.9690, compared to 0.7624 for DiffusionSat and only 0.2170 for SeeoSytth-Canp This TCS margin indicates TAMMs' superior ability to produce temporally coherent futures grounded in prior dynamics.
These quantitative gains are visually reflected in Figures 50aures55andd 6 where TAMMs yields more faithful change summaries and future predictions with better temporal structure.
4.3 ABLATION STUDIES
While above results demonstrate overall superiority of TAMMs, understand the specific tributions of key architectural innovations, we conducted a series of ablation studies, with results presented in Table 2 confirms each proposed module is essential for the framework's 's performance.
For the temporal understanding task, removing PTE and CTP modules leads to significant perfor- mance degradation, most notably dropping TCS score by 23% and 15% respectively. This demon- strates their critical role in awakening MLLM's ability to comprehend temporal dynamics accurately. For future forecasting task. results highlight the importance of our MLLM- -giided approach. Dis- abling semantic feature fusion ('w/o Semantic Fusion') causes substantial 18% drop in TCS (from 0.9690 0.7911), more severe impact than removing only high-level text guidance. This finding validates our core hypothesis: injecting the MLLM's deep semantic reasoning directly into the gen- crative control path is paramount for ensuring long-term temporal consistency. The 'Base Control Block' variant, which lacks both forms of semantic guidance, performs the worst, confirming that performance.
all components are complementary and necessary for achieving
To ensure evaluation's robustness, analyzed the average length of the generated descriptions. As detailed in Appendix(A.3. TAMMs is the only model whose output length closely aligns with the ground-truth references. confirming its superior metric scores not artifact of generation length.
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2015-3-13
2015-6-10
2015- 10-12
2016-7-27
Geo Canny
DiffusionSat
MCVD
TAMMs
2014-1-24
2016-1-5
2016-8-25
2016 12-30
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DiffusionSal
MCVD
TAMMs
2014-10-3
2014 4-
106-11-2 223
2015-7-28
2015- 10-20
Geo-Canny
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TAMMs
Figure 6: Generated samples from our temporal prediction task. Left columns are input sequences and target (with red dates indicating the target images to be predicted); right are predictions from different models. It can be clearly seen that TAMMs well captures the changing trends in the quence and also ensures good image quality.
Change Description Metries
Image Prediction Metries
Model / Config
B-4f
Mt
R-Lt
C-Df
Pt
L
TCS1
Ablation on Temporal Adaptation (for Change Description)
SFT only
0.2134
0.2901
0.4634
0.7523
0.6842
w/o PTE
0.2387
0.3089
0.4721
0.8234
0.7456
w/o CTP
0.2445
0.3156
0.4678
0.8567
0.8234
Ablation on MILMM Integration (for Image Prediction)
w/o Semantic Fusion
12.0642
0.1596
0.5198
0.7911
w/o Text Guidance
11,9655
0.1709
0.5065
0.9410
Base Control Block
11,8878
0.1520
0.5225
0.7624
Our Full Model
TAMMs (Full)
0.2669
0.3312
0.4690
0.9030
12.0697
0.1831
0.4931
0.9690
Table 2: Ablation studies for both tasks.
RELATED WORKS
5.1 MULTIMODAL LARGE LANGUAGE MODELS IN REMOTE SENSING
Multimodal Large Language Models (MLLMs) have shown exceptional capabilities in integrat-
ing vision and language Wang al. (024bb a). However, their application to SITS is non trivial.
models, even those with video understanding like Qwen2.5-VL Bai et al. (025))
not optimized for the sparse, long-term nature of SITS data Khanna al. (2024). While
satellite-speciff adaptations like SITSCC Peng et al. (2024) and Time-VLM/ Zhong et al. (2025)
have emerged for change captioning, they typically rely handcrafted temporal modules and, cru- cially, are limited to descriptive tasks without generative forecasting capabilities.
5.2 CONTROLLABLE GENERATION FUTURE FORECASTING
Latent Diffusion Models (LDMs) Rombach al. (2022). particularly when combined with spatial control mechanisms like ControlNet Zhang et al. (2023), have set the state of the art in high-fidelity image synthesis. In remote sensing, models like DiffusionSat Khanna al. (2244) have adapted this

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technology using metadata conditioning. The fundamental limitation, however, is that their control signals are typically low-level (e.g., edge maps) or based on simple metadata, lacking guidance from high-level, semantic understanding of evolving temporal narrative. They generate what scene 'looks like', but cannot reason about 'why' it should look that way based on historical changes.
6 CONCLUSION AND FUTURE WORK
In this paper, we addressed the fragmented nature of SITS, where temporal change description (TCD) and future forecasting (FSIF) are treated as disjointed tasks limited by common bottleneck in long-range temporal understanding. We proposed TAMMs, the first unified framework designed to synergistically solve both, built on core hypothesis that empowering MLLM to deeply compre- hend historical dynamics would enable more consistent future forecasting. Our comprehensive ex- periments validate that TAMMs achieves state-of-the- performance on both tasks, significantly outperforming specialist baselines. Critically, its substantial gains on our proposed TCS metric pro- vide strong evidence that unified approach leads to forecasts are demonstrably more consistent with historical evolution, establishing new paradigm for reasoning-base SITS analysis.
Building on this work, several promising research avenues are identified. To enhance forecasting over very long-term horizons and for rare, abrupt events, future work will explore hierarchical tem- poral modeling and the integration of external data sources. To improve the framework' 's efficiency and reliability for real-world applications, and incorporate uncertainty quantification (e.g., through Bayesian approaches) to move beyond de- terministic predictions and provide richer forecasting outputs.
will also investigate model compression techniques
ACKNOWLEDGEMENTS
This work was supported by the Foundation Program of the Key Laboratory of Science and Tech- nology on Complex Electronic System Simulation under Grant 614201001032203, We are grateful to our colleagues and research experts in remote sensing and aerospace for their constructive discus- sions and guidance throughout this work.
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A APPENDIX
A.
ETHICS STATEMENT
Our work strictly follows the ICLR Code of Ethics. This study was conducted without involving any human subjects animal experimentation. We have taken great care to ensure the ethical use of all datasets, including Fmow Fmow/Christie et al. (0018), by strictly adhering to their official usage guidelines. Our research process was designed to prevent any privacy breaches or security vulnerabilities, and no personally identifiable information was used at any point. Furthermore, we are committed to actively addressing potential biases and avoiding discriminatory outcomes. This research is a testament to our commitment to integrity and transparency.
A.2 REPRODUCIBILITY STATEMENT
We have made every effort to ensure our results are fully reproducible. This paper provides a detailed description of experimental methodology, including training steps, model configurations, and hardware specifications. The key component of our work, the TAMMSs is also thoroughly described enable replication. All datasets used, such the publicly available Fmow Christie al.) (2018) dataset, are accessible to the public, ensuring consistent evaluation. We believe these measures provide other researchers with the necessary information reproduce our findings and build upon our work.
A.3 LLM USAGE
In preparing this manuscript, we employed a Large Language Model (LLM) as a supportive writing tool. The LLM's role was confined to assisting with textual refinement, focusing improving the clarity, readability, and linguistic quality of the paper. This included tasks such as rephrasing for better flow and correcting grammatical errors.
We want to be explicit that the LLM was not used for any part of the core scientific process. It did not contribute to the research's 's conceptual framework, the experimental methodology, the analysis
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and interpretation of results. All scientific content, ideas, and conclusions the sole responsibility of the authors. By transparently stating our use of the LLM, we commit to a high standard of research integrity. We have thoroughly vetted all LLM-assisted text to ensure its accuracy and originality. and we take full accountability for all content presented in this paper.
A.4 CONTEXTUAL. TEMPORAL. PROMPT
Scene: [Scene Description]. Describe specific changes between these time-series remote sensing images in single paragraph. Focus on concrete changes to structures, landscape, or development with precise location details.
<image>...
A.5 PROMPT ROBUSTNESS ANALYSIS DETAILS
To evaluate the robustness of our Contextual Temporal Prompting (CTP), we conducted an addi- tional ablation study using three variants of prompts with varying levels of detail to test the stability of our model:
Phort: "Describe the changes in the satellite images." (Minimal instruction).
Pours (Default): The structured prompt used in the paper (see Appendix A.4).
Pverbose:
A complex prompt adding approximately 50 words of context about specific land-cover
types.
The full text for the Peerboe prompt is follows:
<image>... <image> Scene: [Scene Description]. Analyze the provided satellite image time series with particular attention to diverse land-cover categories, including urban infrastructure, agricultural vegetation patterns, and hydrological features. Please distinguish between transient seasonal effects (e.g., phenological changes) and permanent structural developments. Based on this context, describe specific changes between these time-series remote sensing images in single paragraph. Focus on concrete changes to structures, landscape, or development with precise location details.
The quantitative results are presented in Table As shown, the performance variance is minimal (TCS fluctuates < 0.015). This demonstrates that TAM is robust: the learnable visual alignment plays the primary role, while the prompt serves high-level task trigger.
Table 3: Prompt Robustness Analysis.
Prompt Type
TCS
BLEU-4
CIDEr-D
P Pshort
0,9580
0.2610
0.8910
Pours (Default)
0.9690
0.2669
0.9030
Pverbose 0.9615
0.2675
0.8980
A.6 TRAINING STRATEGY FOR GENERATIVE MODULE
Training the generative component of TAMMs, particularly the Enhanced Control Module (ECM), requires a carefully designed strategy to ensure stable learning and effective fusion of structural and semantic information. A naive joint training approach risks suboptimal performance, the model may struggle to learn low-level spatio-temporal dynamics while simultaneously trying to interpret high-level semantic guidance. To address this, propose two-stage training procedure
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designed to decouple these learning objectives, prevent catastrophic forgetting of structural priors, and facilitate a robust integration of semantic control.
The trainable parameters of the ECM partitionned into two sets: the structural components Ost, which comprise the 3D Control Blocks responsible for capturing physical spatio-temporal patterns, and the semantic-temporal components 0,-t. which include the Semantic Processors, Gated Fusion units, and Temporal Transformers responsible for translating the MLLM's reasoning into guidance.
A.6.1 STAGE I: STRUCTURAL CONTROL PRE-TRAINING
The primary goal of this stage is to teach the model the fundamental dynamics spatio- temporal evolution based purely the visual information within the SITS sequence. This stage establishes a robust structural foundation for the model.
Objective:
Setup: We freeze the parameters of the pre- pre-trained LDM U-Net (Ou-Net) and the entire MLLM module. Only the structural parameters 4st of the ECM are set to trainable. The semantic path of the ECM is bypassed by zeroing out its output, meaning the conditioning signal injected into the U-Net decoder derived solely from the structural path (z) is derived from h ((trl))
Loss Formulation: Training minimizes the standard LDM diffusion loss. The noise prediction network Eg is conditioned only on the noisy latent 2y, the timestep т, and the U-Net's internal encoder features (h, (ene)) The loss is formulated as:
||c c0(2r, T| {h, (h(on)))
Ssuge 1 -Exo.o-N0..T1
J: U-N Gtrain)
(9)
By backpropagating only through Osc, this stage effectively pre-trains the 3D Control Blocks to extract meaningful and coherent temporal structures from the U-Net's latent space.
A.6.2 STAGE 2: SEMANTIC AND TEMPORAL REFINEMENT
With strong structural prior established, the goal of this stage to learn how ef- fectively translate the MLLM's high-level semantic understanding (M,) into fine-grained control signals and adaptively fuse them with the pre-trained structural features.
Objective:
Setup: We keep the LDM U-Net (fu-Net) and the now pre-trained structural parameters (0st) frozen. The MLLM module is active (but also frozen) to provide the semantic context vector M, Only the semantic-tempor parameters 0,-, are set to be trainable. The full SFCI pathway, including the adaptive gated fusion, now active.
Loss Formulation: Training again minimizes the diffusion loss, but the noise prediction network now additionally conditioned on the semantic vector M,. The gradients are computed only with respect to 0.-1:
Esuge 2
M, Nx ||c- ca (2r,7 (b(en))
(enc) M, U. Nets
Gfrozen garaia
11
(10)
This staged approach is critical. By freezing the structural path, we prevent the powerful seman- tie signals from disrupting the already learned, delicate spatio-temporal priors. The adaptive gating mechanism (Eq. 5) can then focus solely learning the optimal balance between these two informa- tion streams, leading to a stable and effective integration of high-level reasoning into the generative process.
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ot bes with some trees and sattered stregghntt. There the (hnnggh the aftle anage, and there are urcuenoaal beldiiggas belliiiing
long smrgu 3ke with
cumeoss beiklings fiacky
The
peliueily redioused and ad whitewedlll, amuzged
appearte induatra
mall comples are green
runurr..
fedEs and of
imsgea dipict
The trugee deplet
clansiccio mnnn helkfigg ge comembeed by poord aurounding bedteapeee enio and spunch regettiod. irskk, mush alasted scar liglnar..
dialind cryy fidia, clanesrisal by marsis of
ponilal etripe A namow atr demerroggratn
Figure Samples of Satellite Image Time Series captions TAMMs used.
A.7 DATASET DETAILS
A.7.1 DATASET GENERATING PROMPTS
Prompt for Scene Description Generation
You are given set of remote sensing images of the same geographical area captured at different time points. Your task is to produce a concise but detailed description of the scene itself, based the entire set of images. Do not describe any changes temporal differences across the images. Please follow these specific instructions:
1. Objective: Describe the physical and functional characteristics of the area shown across the entire image set within 50 words. Treat the images as a unified snapshot single location.
2. Content Scope: Your description should integrate and reflect much specific and fine-grained detail as possible, including but not limited to:
Natural features (e.g., rivers, forests, vegetation patterss, terrain types)
Artificial structures (e.g., buildings, roads, bridges, industrial zones, residential areas)
Spatial layout and distribution (e.g., clustered housing, grid-like road net- works, green spaces between zones)
Geographical context (e.g., inland plain, coastal region, mountainous area) Probable functional uses or land usage (e.g., commercial, agricultural, recre- ational, mixed-use)
3. Writing Style:
Maintain a scientific and objective tone. Avoid speculation unless supported by
clear visual evidence.
Emphasize spatial relationships and layout patterns when relevant (e.g.. "A dense network of roads connects uniformly spaced residential blocks").
Use precise, domain-appropriate language to ensure clarity and specificity.
Avoid vague adjectives (e.g., "nice," "big") in favor of quantifiable structural descriptors.
4. Exclusion Note:
You must not mention or infer any temporal changes, differences between time points, or dynamic aspects. The output should reflect only the static characteristies of the observed area.
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Prompt for Temporal Change Description Generation
{category). You are looking at a time series of remote sensing images captured over the same geographical location across multiple time points. Your task is to generate a description, in English, that highlights and summarizes only the specific changes observed from the earliest to the latest image. Your output must be:
Scene:
1. Length Constraint: Strictly limited under 50 words (even though initial anal- ysis may consider 77). Be as concise and informative as possible.
2. Focus on Changes Only:
Do not describe the general scene or static elements.
Report concrete, observable changes over time.
3. Types of Changes to Identify:
Structural developments:
newly built or demolished buildings, roads, runways, industrial facilities, housing blocks.
Landscape transformations: deforestation, vegetation expansion/reduction,, coastline shifts, mining excavation.
farmland turned into residential zone, industrial zones expanding into greenfields, infrastructure upgrades.
Functional conversions:
4. Style and Precision:
Use precise spatial references (e.g., "new building in bottom-left corner," "road extended eastward," "tree cover lost near central canal").
Maintain a factual, scientific, and neutral tone no speculation or subjective descriptions.
Use clear, dopaon-appropriiae vocabulary for spatial and structural changes.
Your output should clearly reflect the differences over time, not general charac- teristies of the area. Prioritize meaningful, observable developments with accurate location cues.
Reminder:
A.7.2 SCENE CAPTIONS SAMPLES
Figure presents samples of Satellite Image Time Series captions generated by Qwen2.5-VL Bai et al. (005)..
A.7.3 DATASET DISTRIRUTION
The dataset utilized for training TAMMs is meticulously curated from the large-scale FmoW bench- mark. Recognizing the specific demands of temporal change understanding and prediction tasks, implemented a selection process targeting high-quality, multi-temporal sequences. This process in- volved filtering for sequences with sufficient temporal coverage (e.g., minimum number of distinct timestamps) and exhibiting discernible changes relevant to common land use and environmental dynamics.
The resulting curated dataset comprises 37003 high-fidelity temporal sequences, representing 58 distinct and fine-grained semantic categories. These categories encompass a diverse range of real- world scenarios, including detailed infrastructure developments (e.g., airport terminal expansion, solar farm construction), agricultural shifts (e crop field rotation, irrigation system changes), and residential area evolution, ensuring robust testbed for fine-grained temporal analysis.
Figure g provides a visual summary of the category distribution within this refined dataset. The visualization employs dual-axis format: the bars (left Y-axis) depict the absolute number of se- quences per category, while the line plot (right Y-axis) shows the corresponding percentage distri- bution. Color coding distinguishes categories based on their relative frequency within curated set. While some long-tail characteristics persist, reflecting real-world data distribution, our filtering ensures that the dataset primarily consists of clear, high-quality examples suitable for training and
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Figure 8: Category distribution in the remote sensing dataset. The bar chart (left Y-axis) shows the sample counts per category. A dual axis line plot (right Y-axis) overlays the relative percentage each category contributes to the total dataset. Categories are sorted in descending order of frequency. Colors encode category rank: red (top 10), orange (top 20), yellow (mid range), blue (long tail).
evaluating sophisticated temporal-aware models that require fine-grained understanding of changes over time.
A.8 CONCRETE GENERATED TEXT LENGTII ANALYSIS
To address potential concerns about the sensitivity of automatic evaluation metrics (e.g., BLEU, CIDEr-D) to the length of generated text, we conducted a comprehensive analysis of the average word count for all generated descriptions compared to the human-annotated ground truth references on our test set. The results are presented in Table
Model
Ave Length (words)
A from GT (words)
Ground Truth
30.89
0.00
RSICC-Former
15.20
15.69
SITSCC
19.03
11.86
DeepSeek-VI.2
34.55
3.66
TAMMs (Ours)
32,23
1.34
Table 4: Average word count analysis. The 'A from GT column shows the absolute difference from the Ground Truth length, highlighting that TAMMs' output length is most aligned with human references.
The analysis reveals significant insights into the quality and nature of the descriptions produced by different models.
Under-generatiin by Baselines: Prior specialized methods like RSICC-Former (15.20 words) and SITSCC (19.03 words) produce descriptions that are substantially shorter than the ground truth average of 30.89 words. Their length difference of 15.69 and 11.86 words, respectively, indicates a significant inability to capture the full complexity and scope of the temporal changes present in the satellite imagery sequences.
Over-generation by General MLLM: In contrast, the general-purpose DeepSeek-Vis VL2 model generates slightly longer descriptions (34.55 words) than the ground truth. While closer in length, its lower scores on semantic quality metrics (Table 1) suggest that this extra length does not nec- essarily translate to higher quality, potentially indicating verbosity or less relevant details.
Optimal Alignment by TAMMs: Our model is the only that produces descriptions with aver- age length (32.23 words) that is remarkably close to the ground truth, with a minimal difference of only 1.34 words. This close alignment strongly supports the conclusion that our model's superior
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performance in automatic metrics stems from genuinely higher-quality generation that compre- hensively and concisely captures the key temporal changes, rather than from any length-relate bias in the evaluation metrics. TAMMs achieves the optimal balance of comprehensive temporal coverage while maintaining the appropriate descriptive scope.
A.9 TRAINING DETAILS
A.9.1 MODEL ARCHITECTURE AND HYPERPARAMETERS
We adopt U-Net architecture based on DiffusionSat for the core diffusion model, enhanced with our MLLM-basee temporal understanding modules (PTE, CTP) and the Semantic-Fused Control Injection (SFCT) mechanism via the Enhanced ControlNet Module. Key architectural choices and default hyperparameters used during training are summarized T Note that specific learning rate schedules varied across training stages as detailed below.
A.9.2 MULTI-STAGE TRAINING STRATEGY
The training process for our Enhanced Control Module (ECM) follows carefully designed three- stage strategy. This is crucial to effectively integrate semantic guidance from the MLLM while preserving the foundational structural control learned by the base 3D Control Blocks. The stages below focus specifically optimizing the diffusion loss (Lan E [|le c0(2,, c,ndd) for the Enhanced ControlNet Module and ensuring stable integration:
Stage 1: Structural Control Pre-training. The initial stage focuses on learning the foundational spatio-temporal dynamics. We freeze the U-Net and MLLM, training only the 3D Control Block weights (0ar). This allows the model to learn to extract structural priors from the image sequence alone, without semantic influence. This stage trained for approximately 50k steps using the Adam W optimizer Kingma & Ba (014) with a constant learning rate of 2e-5.
Stage 2: Initial Semantic Fusion Training. With the structural path frozen, we then introduce the MLLM's semantic guidance (M,). In this stage, train only the semantic path components (0.-r), including the Semantic Processors and Gated Fusion modules. This teaches the model how to translate the MLLM's abstract understanding into features compatible with the generative process. This stage also used a constant learning rate of 2e-5.
Stage 3: Joint Fine-tuning with Cosine Decay. Finally, we fine-tune the semantic path compo- nents (0.-z) achieve optimal convergence and stability. The key difference this stage is the learning rate schedule, which is switched to a Cosine Annealing scheduler. This allows for a more refined integration of the semantic guidance with the now-robust structural priors, leading to the final model performance.
Condensed Two-Stage Training Flow Initialize: Frozen LDM U-Net, Frozen MLLM, Trainable ECM components (8sr, 6z-1).
Stage 1: Structural Control Learning
Goal: Learn spatio-temporal structure from U-Net features.
Active Components: 3D Control Blocks (0st).
Inactive: MLLM semantic input (M,), Semantic/Tempont ECM parts (0.-z).
Training: Minimize LDM loss Lsaae w.r.t. only 0st-
Stage 2: Semantic and Temporal Refinement
Goal: Integrate MI.MM guidance and refine temporal consistency.
Active Components: Full ECM path using frozen 0₂t and MILM context M,. Trained: Semantic Processors, Gates, Temporal Transformers (0.-2).
Training: Minimize LDM loss LSnge 2 w.r.t. only 0.-t-
Output: Trained ECM (0st from Stage I, 0,-: from Stage 2).
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A.9.3 TRAINING INFRASTRUCTURE
All experiments were conducted 2xNVIDIA A6000 GPUs using PyTorch 2.0.1. Mixed-precision training (AMP) was utilized to optimize memory usage and training speed. Training progress and metries were logged using wandb.
A.9.4 Loss CURVES
Figure 9 illustrates the training loss progression. The top plot shows the loss associated with the tem- poral change description task (e.g., MLLM'ss text output loss). The bottom plot displays the diffusion loss (Lair) for the image gener- ation component across the three training stages described in Sec- tion [A.92] Stage (approx. 50k steps, green) shows the initial conver- gence of the structural control path. Stage (approx. 50k-70k steps, purple/pink) corresponds to the in- troduction and training of seman- tic fusion components with stant learning rate. Stage 3 (approx. 70k+ steps, blue/teal) demonstrates the fine-tuning phase using cosine learning rate decay, leading to fur- ther stabilization and refinement of the diffusion loss. The overall stable convergence across stages validates our training strategy.
Parameter
Value
Epochs
100
Batch Size
12
Gradient Accumulation
Initial I.caningg Rate
2e-5
Optimizer
Adam
Default Scheduler
Cosine Annealing (Stage 3)
Max Grad Norm
1.0
AMP (Mixed Precision) Workers
Enabled
12
Model Max Length
512
Input Sequence Length Prediction Length Patch Size
3
16
Patch Stride
U-Net Encoder Dim Feedforward Dim Attention Heads Dropout Rate
1024
2048
8
0.1
Visual Feature Dim Conditioning Dim (Diffusion)
1280
512
Vision Mapping Layers
Text Feature Dim
512
Text Output Dim
1024
Fusion Dim
1024
Use Contrastive Loss
(weight 0.1)
Use MI.LM Features
Dual Modality Output
Text Loss Enabled
Output Attention Map
Table 5: Training configuration and architectural hyperpa- rameters.
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Published as a conference paper at ICLR 2026
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Figure 9: Training loss curves. (Top) Loss for the temporal change description task. (Bottom) Diffusion loss for the image generation component, illustrating the three training stages: Stage (3D Control Block Pre-training, green region), Stage (Semantic Fusion Training with constant LR, pink/purple region), and Stage 3 (Semantic Fusion Fine-tuning with cosine LR, blue/teal region). Steady convergence is observed all stages.
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Reviews:
[{"review_id": "WROYyLpePf", "reviewer": "Reviewer_zohA", "summary": "The paper proposes TAMMs, a unified framework that connects TCD with FSIF. The approach freezes a multimodal LLM and introduces TAM for long-range temporal reasoning, and injects these semantics into a frozen diffusion U-Net via SFCI to guide fine-grained generation. A new metric, TCS, evaluates whether predicted changes align spatially and in area with historical change. Extensive experiments on a large fMoW training corpus and long-horizon Google Earth sequences show strong TCD performance and notable gains on TCS, with ablations supporting the contributions.\nStrengths", "questions": "1. How sensitive is TCS to the underlying change detector, its thresholds, and hyperparameters (e.g., σ, β)? Any correlation with human rankings?\n\n2. Could you clarify cases where SSIM/PSNR lag slightly behind certain baselines—are there domain/normalization or texture–sharpness trade-offs?\n\n3. How do you handle geometric reprojection (CRS/EPSG), the resampling kernel, and radiometric normalization across time?\n\n4. What is the exact mask post-processing (e.g., thresholding, morphology) before centroid and area computation?\n\n5. Could you compare TCS against temporal FID/KID variants or other spatial-consistency metrics to validate complementary signal?", "limitations": "", "rating": 4, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Timely problem and clear unification, bridging understanding to forecasting in a single, parameter-efficient pipeline for remote-sensing time series.\n\n2. Well-motivated architecture: TAM adds lightweight temporal conditioning without full fine-tuning; SFCI combines structural and semantic control for diffusion, yielding an intuitive, modular design.", "weaknesses": "1. On FSIF, some standard metrics are only competitive rather than strictly superior to strong generative baselines. A brief discussion and supplemental visuals would help reconcile this with the TCS gains.\n\n2. Prompt robustness is missing: TAM relies on Contextual Temporal Prompting (CTP), but there is no analysis of robustness to prompt phrasing/length/templates; please add prompt ablations.\n\n3. Test-time generalization is not demonstrated. Evaluation hinges on a curated set of 150 long-horizon Google Earth sequences; please provide cross-dataset (e.g., Sentinel-2/Landsat) or leave-region/season-out tests.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "wDVclJxfOj", "reviewer": "Reviewer_7xU9", "summary": "The paper presents TAMMs, a unified framework for jointly performing Temporal Change Description (TCD) and Future Satellite Image Forecasting (FSIF) within a single MLLM-diffusion architecture. It introduces two main components: Temporal Adaptation Modules (TAM), which enhance the model’s ability to capture long-range temporal dynamics, and Semantic-Fused Control Injection (SFCI), which transfers temporal understanding into precise generative control. This integration allows insights from TCD to directly improve FSIF consistency and performance across both tasks.", "questions": "- As far as i understand captions generated via Qwen 2.5 VL are treated as ground truths for training. Can you explain why the trained model outperforms Qwen 2.5?\n- in l.199 can you explain what is meant via \"specific time interval\", what is the input to the small MLP?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Synergistic training for change detection together with future generation is an interesting idea for enhancing the reasoning capabilities of MLLM in the satellite image domain.\n- Temporal Consistency Score (TCS) adds value as a metric specifically desgned for application on SITS.\n- Performance results are strong in Table 1.\n- Ablation study is performed on proposed modules.", "weaknesses": "- Some examples of the data used for training (satellite images together with generated captions) should be presented, possibly in supp. material.\n- writing could be improved, some non exhaustive examples for use of language include:\n   - l.38 \"because fails\"\n   - l.39-41 \"the absence of a unified framework of temporal-aware multimodal model for satellite image change understanding and forecasting named TAMMs\"\n   - l.42 \"How can model reason\"\n- similarly \"Satellite Image Time Series (SITS)\" appears in several places in text, one should be ok then use abbreviation", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "7ARme1tmj9", "reviewer": "Reviewer_kXkZ", "summary": "This paper presents a framework called **TAMMs**, designed for temporal change description and future image forecasting in satellite image time series. The method builds upon a frozen MLLM and a diffusion-based generator, introducing two auxiliary modules — **Temporal Adaptation Module (TAM)** and **Semantic-Fused Control Injection (SFCI)** — to enable temporal awareness and semantic guidance without large-scale fine-tuning. The TAM module enhances the MLLM’s temporal reasoning through Physical Time Encoding (PTE) and Contextual Temporal Prompting (CTP), while the SFCI module maps the MLLM’s semantic outputs into multi-scale control signals to guide the diffusion generation process. The paper further proposes a new metric, **Temporal Consistency Score (TCS)**, to quantify the consistency between predicted changes and historical dynamics. Experiments conducted on the fMoW (training) and Google Earth (testing) datasets demonstrate that the proposed approach outperforms several existing baselines on both temporal description and image forecasting tasks.", "questions": "See Weaknesses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 1, "contribution": 2, "strengths": "1. The paper provides a unified framework for satellite image time-series modeling, integrating semantic understanding and future prediction into a single closed-loop process.\n2. The proposed TAM and SFCI modules are logically designed to enable temporal encoding and generative control while keeping the pretrained MLLM and diffusion models frozen.\n3. The introduction of the TCS metric offers a way to quantify spatial consistency between predicted and historical changes, addressing the limitation of conventional image quality metrics in temporal evaluation.", "weaknesses": "1. The study focuses narrowly on satellite image forecasting and does not explore the applicability of the proposed framework to other domains, limiting its generality.\n2. The proposed approach appears primarily as an engineering implementation based on pretrained LLMs and diffusion models, with limited introduction of new theoretical mechanisms or modeling principles; thus, its novelty is modest.\n3. The overall architecture is complex but lacks a clear explanation of the core ideas and hierarchical relationships among key components. The importance, dependency, and training connections of different modules are not well clarified. The paper allocates substantial space to implementation details and equations but does not adequately explain the design rationale or training phase distinctions. For example, while the appendix mentions multi-stage training, the main figures do not distinguish which components are frozen or trainable across different stages, which makes the workflow difficult to interpret.\n4. Other issues: The title in the PDF and the OpenReview submission are inconsistent and should be unified; Equation (6) is missing a pair of braces.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "lUhr4o9gCc", "reviewer": "Reviewer_72WA", "summary": "This paper presents TAMMs, the first unified framework that jointly performs temporal change description (TCD) and future satellite image forecasting (FSIF) by enhancing long-range temporal understanding. It introduces lightweight Temporal Adaptation Modules (PTE and CTP) to awaken a frozen MLLM’s ability to reason over sparse, long-term satellite image time series, producing a semantic feature Mt. The novel Semantic-Fused Control Injection (SFCI) mechanism then translates Mt into multi-scale, spatially-aware control signals to guide a frozen diffusion U-Net, ensuring patch-level temporal consistency in generated future images. Additionally, a new metric, Temporal Consistency Score (TCS), is proposed to quantify the plausibility of predicted changes against historical dynamics. Extensive experiments demonstrate that TAMMs significantly outperforms state-of-the-art specialized baselines on both tasks, with particularly large gains in TCS, validating the closed-loop paradigm of “understanding why changes occur to forecast what will happen.", "questions": "1.It is reasonable to encode time_diff into the input sequence, but why make it a learnable token, I do not think it is to“inject explicit temporal awareness” but implicit instead. The author should provide some persuasive ablation studies here.\n\n2.In figure 5, apart from TAMMs generate the most detailed description, it is obvious that the description length of TAMMs is much longer than that of other models，  making it appear to be an unfair comparison, which is worth noting", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "TAMMs pioneers a single, frozen-backbone framework that couples satellite change captioning with future image synthesis, leveraging lightweight adapters to awaken long-range temporal reasoning and fine-grained diffusion control; it handles sparse, multi-year sequences, introduces the TCS metric to quantify temporal plausibility, and achieves comprehensive SOTA results with minimal trainable parameters.", "weaknesses": "1. the paper presentation need to be improved.\nFor example, TAMMs is designed through different modules like PTE, CTP, SFCI, however there are no corresponding label in figure 2, making it not convenient for readers to understand.\nIn addition, the descriptive labels are automatically generated by Qwen2.5-VL model, why in figure 2 it is ChatGPT icon?\n\n\n2. The ablation study is not sufficient.\nThe choice of MLLM backbone(why DeepSeek-VL2) is not clear, as well as the choice of Stable Diffusion\nIn addition, in table2 Ablation on Temporal Adaptation (for Change Description), w/o PTE achieves sota and authors should provide corresponing analysis", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)