Satellite imagery is increasingly available, high resolution, and temporally detailed. Changes in spatio-temporal datasets such as satellite images are particularly interesting as they reveal the many events and forces that shape our world. However, finding such interesting and meaningful change events from the vast data is challenging. In this paper, we present new datasets for such change events that include semantically meaningful events like road construction. Instead of manually annotating the very large corpus of satellite images, we introduce a novel unsupervised approach that takes a large spatio-temporal dataset from satellite images and finds interesting change events. To evaluate the meaningfulness on these datasets we create 2 benchmarks namely CaiRoad and CalFire which capture the events of road construction and forest fires. These new benchmarks can be used to evaluate semantic retrieval/classification performance. We explore these benchmarks qualitatively and quantitatively by using several methods and show that these new datasets are indeed challenging for many existing methods.

In this supplementary material we present more information about the dataset (including a datasheet for the dataset) and extensive results that could not fit in the main paper. In sec. 2 we include a datasheet for our dataset. In sec. 4 we look at the statistics of our two benchmarks CalFire and CaiRoad. The data is publicly available at https://www.cs.cornell.edu/projects/ Our code for accessing Sentinel-2 images, creating change events and baselines can be found at https://github.com/utkarshmall13/ We include a datasheet for our dataset following the methodology from "Datasheets for Datasets" [7]. In this section we include the prompts from [7] in blue and in black are our answers. Was there a specific task in mind? Was there a specific gap that needed to be filled? The dataset was created to foster research on the problem of automatic discovery and semantic understanding of change events in satellite imagery. More specifically, the dataset should aid in developing systems that can automatically detect change events in satellite imagery and assign to each a semantic label that indicates the nature of the event, e.g., forest fires, road construction etc. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The dataset contains RGB bands from Sentinel-2 satellite imagery. Users should keep in mind that changes smaller than the resolution be undetectable. For example, changes to roofs of houses, movements of traffic will not be detected. The datasets should be used for larger changes such as forest fire, crop changes etc. 2.2 Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)?

In this supplementary material we present more information about the dataset (including a datasheet for the dataset) and extensive results that could not fit in the main paper. In sec. 2 we include a datasheet for our dataset. In sec. 4 we look at the statistics of our two benchmarks CalFire and CaiRoad. The data is publicly available at https://www.cs.cornell.edu/projects/ Our code for accessing Sentinel-2 images, creating change events and baselines can be found at https://github.com/utkarshmall13/ We include a datasheet for our dataset following the methodology from "Datasheets for Datasets" [7]. In this section we include the prompts from [7] in blue and in black are our answers. Was there a specific task in mind? Was there a specific gap that needed to be filled? The dataset was created to foster research on the problem of automatic discovery and semantic understanding of change events in satellite imagery. More specifically, the dataset should aid in developing systems that can automatically detect change events in satellite imagery and assign to each a semantic label that indicates the nature of the event, e.g., forest fires, road construction etc. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The dataset contains RGB bands from Sentinel-2 satellite imagery. Users should keep in mind that changes smaller than the resolution be undetectable. For example, changes to roofs of houses, movements of traffic will not be detected. The datasets should be used for larger changes such as forest fire, crop changes etc. 2.2 Composition What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)?

Satellite imagery is increasingly available, high resolution, and temporally detailed. Changes in spatio-temporal datasets such as satellite images are particularly interesting as they reveal the many events and forces that shape our world.

Data-driven deep learning models have enabled tremendous progress in change detection (CD) with the support of pixel-level annotations. However, collecting diverse data and manually annotating them is costly, laborious, and knowledge-intensive. Existing generative methods for CD data synthesis show competitive potential in addressing this issue but still face the following limitations: 1) difficulty in flexibly controlling change events, 2) dependence on additional data to train the data generators, 3) focus on specific change detection tasks. To this end, this paper focuses on the semantic CD (SCD) task and develops a multi-temporal SCD data generator ChangeDiff by exploring powerful diffusion models. ChangeDiff innovatively generates change data in two steps: first, it uses text prompts and a text-to-layout (T2L) model to create continuous layouts, and then it employs layout-to-image (L2I) to convert these layouts into images. Specifically, we propose multi-class distribution-guided text prompts (MCDG-TP), allowing for layouts to be generated flexibly through controllable classes and their corresponding ratios. Subsequently, to generalize the T2L model to the proposed MCDG-TP, a class distribution refinement loss is further designed as training supervision. %For the former, a multi-classdistribution-guided text prompt (MCDG-TP) is proposed to complement via controllable classes and ratios. To generalize the text-to-image diffusion model to the proposed MCDG-TP, a class distribution refinement loss is designed as training supervision. For the latter, MCDG-TP in three modes is proposed to synthesize new layout masks from various texts. Our generated data shows significant progress in temporal continuity, spatial diversity, and quality realism, empowering change detectors with accuracy and transferability. The code is available at https://github.com/DZhaoXd/ChangeDiff

Understanding the temporal dynamics of Earth's surface is a mission of multi-temporal remote sensing image analysis, significantly promoted by deep vision models with its fuel -- labeled multi-temporal images. However, collecting, preprocessing, and annotating multi-temporal remote sensing images at scale is non-trivial since it is expensive and knowledge-intensive. In this paper, we present a scalable multi-temporal remote sensing change data generator via generative modeling, which is cheap and automatic, alleviating these problems. Our main idea is to simulate a stochastic change process over time. We consider the stochastic change process as a probabilistic semantic state transition, namely generative probabilistic change model (GPCM), which decouples the complex simulation problem into two more trackable sub-problems, \ie, change event simulation and semantic change synthesis. To solve these two problems, we present the change generator (Changen), a GAN-based GPCM, enabling controllable object change data generation, including customizable object property, and change event. The extensive experiments suggest that our Changen has superior generation capability, and the change detectors with Changen pre-training exhibit excellent transferability to real-world change datasets.