---
title: 'Gaza-Change Dataset: Damage & Change Analysis'
url: https://www.emergentmind.com/topics/gaza-change-dataset
type: topic
---

# Gaza-Change Dataset: Damage & Change Analysis

The Gaza-Change Dataset is a collective designation for several open-access and request-based datasets providing high spatiotemporal resolution damage and semantic change information for the Gaza Strip, 2023–2024. These resources offer varying granularity—pixel-level semantic masks, building-level damage classifications, and raster coherence-change time series—derived from high-resolution optical and synthetic aperture radar (SAR) imagery. The datasets facilitate fine-grained inference and systematic monitoring of conflict-induced damage, supporting research, humanitarian response, and reconstruction planning.

## 1. Geographic and Temporal Scope

The Gaza-Change datasets comprehensively cover the Gaza Strip, centered at latitudes 31.4°–31.6° N and longitudes 34.4°–34.6° E. They encompass nine major urban localities, including Khan Yunis and Rafah. Temporal coverage varies by modality and task:

- **Optical/semantic:**
  - Bi-temporal Beijing-2 imagery (3.2 m GSD), patch-based, with two dates per scene during 2023–2024.
- **SAR-based:**
  - Sentinel-1 C-band GRD and SLC data, with reference/inference periods spanning October 2023 – October 2024.
  - InSAR monitoring at up to weekly cadence using 321 SLC scenes [2506.14730].

These datasets are designed for damage assessment, change detection, humanitarian logistics, and longitudinal urban impact studies [2511.19035, 2405.06323].

## 2. Data Sources, Structure, and Annotation Protocols

### Gaza-Change (High-Resolution Optical, Pixelwise Semantics)

- **Imagery:** Beijing-2 satellite, 3.2 m GSD, 512×512 px patches.
- **Change annotation:** Only pixels exhibiting semantic change between $T_1$ and $T_2$ are labeled; background remains unlabeled (implied “no-change”).
- **Semantic classes (6 total):**
  - Building Damage
  - New Building
  - New Camp
  - Farmland Damage
  - Greenhouse Damage
  - New Greenhouse

Annotations are exclusively change masks, omitting full-scene semantic segmentation. This approach reduces annotation cost and focuses on change detection over static land cover labeling, mitigating error propagation from multi-stage annotation [2511.19035].

### SAR-Based Damage Datasets

- **PWTT (Pixel-Wise T-Test):** Area-weighted damage classification at the building footprint level (201,629 footprints) using Sentinel-1 GRD (10 m resolution, both VV and VH polarizations, ascending/descending).
  - Binary label per footprint: “damaged” (UNOSAT-intersecting) or “undamaged”, with area and mean $T$-statistic [2405.06323].
  - Derived from Microsoft GlobalML building footprints and UNOSAT optical manual damage points as reference.

- **InSAR/LT-CCD:** Time-resolved, building-level coherence-change monitoring (330,079 OSM-based polygons).
  - Per-pixel and building-aggregated coherence anomalies at 40 m resolution, with time of first, last, and confirmed damage flag per building.
  - Raster stack: 56 weekly GeoTIFF grids.
  - Aggregated attributes: pre-war mean and std. coherence, damage flag (binary), fraction of building flagged, confirmation persistence [2506.14730].

## 3. Processing Pipelines and Methodological Frameworks

### Semantic Change Detection (CSD Task)

The CSD framework, defined in [2511.19035], extends conventional binary change detection (BCD) to multi-class semantic change tasks using only change pixels for annotation. Methodological innovations include:

- **Backbone:** Pre-trained DINOv3 for robust representation from bi-temporal inputs.
- **Network:** Multi-scale cross-attention difference Siamese architecture (MC-DiSNet).
- **Masking protocol:** Only changed semantic regions labeled; background pixels avoided in mask annotation.

### SAR-Based Damage Algorithms

- **PWTT:** Pixel-wise disjoint t-tests performed for each orbit/polarization/time period; per-pixel $T$-statistic aggregated per building. Decision threshold for $T$ empirically set ($\tau=2.7$ typical for $\alpha=0.01$), damage label assigned per building depending on threshold exceedance [2405.06323].
- **LT-CCD:** Long temporal-arc InSAR coherence change detection:
  - For each week, form $N$ interferogram pairs between conflict and pre-war epochs.
  - Compute mean coherence $\overline{\gamma}_{\text{conflict}}(t)$, $\overline{\gamma}_{\text{pre}}$, std $\sigma_{\text{pre}}$.
  - Calculate coherence anomaly and z-score: $\Delta\gamma(t) = \overline{\gamma}_{\text{conflict}}(t) - \overline{\gamma}_{\text{pre}}$, $z(t) = \Delta\gamma(t)/\sigma_{\text{pre}}$.
  - Damage criterion: $\Delta\gamma(t) < k$ and $z(t) < -2$ ($k = -0.1$ empirically optimal).
  - Persistence filter: change must persist for at least one repeat acquisition to avoid transient errors [2506.14730].

## 4. Dataset Scale, Class Balance, and Organization

| Dataset/Modality       | Granularity      | N (primary unit)          | Temporal Coverage           | Label Type                             |
|------------------------|------------------|---------------------------|-----------------------------|----------------------------------------|
| Gaza-Change (optical)  | Patch/pixel      | 922 patch pairs (512×512) | 2023–2024 (bi-temporal)     | 6-class pixel-level change masks       |
| PWTT (SAR, S1 GRD)     | Building         | 201,629 footprints        | 2023.10–2024.03 (aggregate) | Binary (damaged/undamaged, per bldg)   |
| LT-CCD (SAR, S1 SLC)   | Building/pixel   | 330,079 buildings         | 2023.10–2024.10 (weekly)    | Damage flag + time series              |

Class distribution is highly imbalanced: change pixels constitute a small minority in optical patches; building damage dominates among classes, with “new camp” and “farmland damage” much rarer. In SAR-derived tables, damaged buildings are ~33.9% (PWTT, 2024.03) and ~57.9% (LT-CCD, cumulative over war) [2511.19035, 2405.06323, 2506.14730].

## 5. Quantitative Benchmarks and Evaluation Metrics

All Gaza-Change models and datasets utilize standard pixel-wise or instance-wise classification, segmentation, and detection metrics—expressed as:

- **OA (Overall Accuracy):** $\frac{\sum q_{ii}}{\sum_{i,j} q_{ij}}$
- **Precision (per class):** $TP_c/(TP_c + FP_c)$
- **Recall (per class):** $TP_c/(TP_c + FN_c)$
- **IoU (per class):** $TP_c/(TP_c + FP_c + FN_c)$
- **Mean IoU (mIoU):** $(1/C) \sum_{c=1}^C IoU_c$
- **F1 (per class):** $2 \cdot \frac{\text{Precision}_c \cdot \text{Recall}_c}{\text{Precision}_c + \text{Recall}_c}$

### MC-DiSNet (Gaza-Change, 6-way CSD):

- Precision: 86.10%
- Recall: 60.98%
- mIoU: 55.16%
- F1: 69.25%
- Per-class F1 ranges: 44.79% (farmland damage) to 86.91% (new greenhouse) [2511.19035].

### PWTT (SAR, building-level, area-weighted):

- AUC: 0.81
- F1: 0.64
- Precision: 0.53
- Recall: 0.82
- N: 201,629 footprints

*Balanced sample (N=273,224):* AUC 0.82, F1 0.76 [2405.06323].

### LT-CCD (InSAR, building-level, validation vs UNOSAT):

- Overall agreement: 92.5%
- True positive rate: 86.2%
- False positive rate: 1.2%
- F1: 91.8%
- CSI: 85.2%
- N: 928,397 UNOSAT point labels [2506.14730].

A plausible implication is that multi-temporal coherence approaches are highly effective for rapid cumulative damage tracking, while semantic optical change datasets offer richer fine-grained category information but with considerably more challenging annotation and class imbalance characteristics.

## 6. Access Modalities and Data Formats

- **Gaza-Change (optical/CSD):** Access to full data and file-format documentation is provided to qualified researchers upon request; formats likely follow de facto patch-wise GeoTIFF for imagery and PNG/TIFF for masks, with associated metadata for date, scene, coordinates, and class legend [2511.19035].
- **PWTT and LT-CCD SAR datasets:** Open access via GitHub and Zenodo (static DOI), providing:
  - Building-level attribute tables (CSV/GeoJSON)
  - Weekly coherence-change GeoTIFF raster stacks (LT-CCD)
  - Metadata JSONs.
- **API/Earth Engine:** RESTful JSON endpoints delivering per-building or per-time step SAR change status; persistent Earth Engine public assets for direct geospatial analysis [2405.06323, 2506.14730].

## 7. Applications, Limitations, and Context

Gaza-Change datasets are essential for near-real-time and retrospective assessment of conflict-induced damage:

- **Primary applications:** Damage mapping, humanitarian logistics, post-conflict planning, trend analysis, and resource allocation.
- **Optical semantic change detection** is challenged by the limited spatial extent of semantic regions, high intra-class similarity, and boundary ambiguity—yielding low percentage of change pixels and significant class imbalance [2511.19035].
- **SAR-based approaches** (PWTT, LT-CCD) enable building-level and area-wide monitoring with timely updates and robust reference-controlled change detection. Their limitations include reduced discriminatory capacity in areas with low pre-conflict coherence and inability to attribute specific semantic class to detected change (damage vs. new construction, etc.) [2405.06323, 2506.14730]. Both validation and false-positive/negative analyses rely on UNOSAT manual optical damage points, considered high-quality reference but potentially missing subtle or partial damages.

Collectively, these datasets anchor open, reproducible, and multi-modal methodologies for damage assessment in conflict zones, providing granular as well as high-frequency monitoring resources intimately tied to operational and humanitarian needs.

Source: https://www.emergentmind.com/topics/gaza-change-dataset