Impact craters play a crucial role in planetary science, as they serve as windows into the geological evolution of planetary surfaces and are key considerations in selecting landing sites. However, most existing crater detection algorithms are either closed-source or difficult to use due to complex configurations, and many are limited to specific celestial bodies, performing well only on simple, flat surfaces. To address these limitations, we present the "Automatic Crater Detection (ACD) Tool," a user-friendly software solution built on deep learning algorithm that was selected through experimental parameter comparisons. By incorporating active learning techniques, the tool continually improves through the inclusion of samples from varying landforms. This allows for accurate crater detection on complex and diverse planetary surfaces, with a minimum detectable crater diameter of 10 pixels. Tested on standard image datasets from the Moon, Mars, and Mercury, the method achieved high accuracy, achieving F1 scores of 92.8%, 91.7%, and 92.2% for the Moon, Mars, and Mercury, respectively. The ACD Tool is wrapped with an interactive user interface that ensures easy utilization, allowing users to generate ready-to-use output data with a simple click. The introduction of ACD Tool can empower scientists with a straightforward yet powerful tool for conducting crater research more efficiently, supporting tasks such as surface age estimation and landing site safety analysis. The software can be downloaded via the following link: https://www.scidb.cn/s/yemuYb.

Derivation of sea surface wind direction is a key step of sea surface wind retrieval from spaceborne Synthetic Aperture Radar (SAR) data. This technical note describes an implementation of the Local Gradient (LG) method to derive sea surface wind directions at a scale of a few kilometers from X-band spaceborne SAR TerraSAR-X (TS-X) data in wide swath mode. The 180° ambiguity in the derived sea surface wind direction is automatically eliminated using a single reference wind direction from external data sources. Several typical cases acquired in the North Sea were presented to demonstrate the derivation of sea surface wind direction under different wind situations using this method. The derived sea surface wind direction were further compared to atmospheric model prediction results. In addition, a practical method is introduced to address ambiguity in the derived sea surface wind directions using the LG method in a typhoon case with rotating surface wind structure. By interpolating the derived wind directions at a scale of kilometers, sea surface wind speeds with a spatial resolution of 500 m are subsequently retrieved using the X-band SAR sea surface wind Geophysical Model Function (GMF). The approach accomplished by combining the LG method with the X-band GMF for deriving sea surface wind in high spatial resolution demonstrates its potential for operational service.

One major pathway for studying lunar volcanism evolution is to construct a timeline for the emplacement of basalts. The crater size‐frequency distribution (CSFD) is a pivotal method for dating lunar basalt units. Building upon two previously published lunar impact crater databases (diameters ≥1–2 km), this study integrated these databases and corrected for latitudinal distortions in crater morphology due to map projection. By utilizing morphological properties such as eccentricity, the depth‐to‐diameter ratio, and local spatial density, an automated classification model was constructed using the support vector machine method to distinguish primary craters from secondary craters superposed on specific geological units in the integrated lunar crater database. After correcting for the spatial variability in lunar impact cratering rates, global chronological maps of both maria and cryptomaria on the Moon were generated. Based on CSFD of craters ≥1 km, the maps indicate that areas southwest of the Aristarchus plateau within the Oceanus Procellarum have the youngest geologic units, with model ages of ∼1.5 Ga. The majority of young lunar mare basalts (3.5 Ga, are located in Maria Tranquillitatis, Nectaris, and Fecunditatis. The model age of the southern mare unit in the Apollo Basin where Chang'e‐6 landed is also in alignment with the radioisotopic ages determined by the recently returned Chang'e‐6 basalt samples. Finally, utilizing these newly updated chronological maps, the temporal variations in lunar basaltic eruption volumes are constrained.