The Google Satellite Embedding dataset is a global, analysis-ready collection of learned geospatial embeddings. Each 10-meter pixel in this dataset is a 64-dimensional representation, or "embedding vector," that encodes temporal trajectories of surface conditions at and around that pixel as measured by various Earth observation instruments and datasets, over a …

L'ensemble de données Google Satellite Embedding est une collection mondiale d'intégrations géospatiales apprises, prêtes pour l'analyse. Chaque pixel de 10 mètres de cet ensemble de données est une représentation à 64 dimensions, ou "vecteur d'intégration", qui encode les trajectoires temporelles des conditions de surface au niveau de ce pixel et autour de celui-ci, telles que mesurées par divers instruments et ensembles de données d'observation de la Terre, sur une période de…

Machine learning model embeddings dataset providing pre-computed feature representations for satellite and aerial imagery analysis.

The satellite data services market encompasses two primary product types: Image Data and Data Analytics. Image Data: Satellite imagery obtained from diverse sensors, providing valuable insights into the Earth's surface and vegetation. Data Analytics: The processing and analysis of satellite data to extract actionable insights and facilitate informed decision-making. This includes advanced techniques such as machine learning and artificial intelligence (AI) for pattern recognition, anomaly detection, and predictive modeling. Recent developments include: For instance,July 2022 Northrop Grumman has contracted Airbus U.S. Space & Defense, Inc. to provide 42 satellite platforms as well as assembly, integration, and test (AIT), launch, and space vehicle commissioning support services to fulfill the Tranche 1 Transport Layer prototype constellation (T1TL) award from the United States Space Development Agency (SDA)., For instance,February 2023 BlackSky is embedding the Inmarsat and Addvalue inter-satellite data relay technology, IDRS, into its next-generation satellite architecture. Addvalue has developed a novel Low-Earth Orbit (LEO) satellite antenna system that provides complete communications coverage when satellites are not visible to a base station. The IDRS terminal makes advantage of Inmarsat's satellite network to provide LEO satellites with continuous communications for mission tasking and data monitoring.. Key drivers for this market are: Technological advancements in satellite technology and data analytics

Government initiatives supporting satellite data utilization

Rising demand for satellite data in various industries. Potential restraints include: Regulatory restrictions on satellite operations

High cost of satellite launch and deployment. Notable trends are: Growing need for satellite imagery data is driving the market growth.

Google 위성 삽입 데이터 세트는 학습된 지리 공간 삽입의 분석 준비가 완료된 전역 컬렉션입니다. 이 데이터 세트의 각 10미터 픽셀은 다양한 지구 관측 기기와 데이터 세트로 측정된 해당 픽셀의 표면 상태 시간적 궤적을 단일 연도에 걸쳐 인코딩하는 64차원 표현 또는 '삽입 벡터'입니다. 밴드가 물리적 측정을 반영하는 기존 스펙트럼 입력 및 지수와 달리, 삽입은 다중 소스, 다중 모달 관찰 간의 관계를 직접적으로 해석할 수는 없지만 더 강력한 방식으로 요약하는 특징 벡터입니다. 이 데이터 세트는 조간대 및 산호초 지대, 내륙 수로, 연안 수로를 비롯한 육상 지표면과 얕은 물을 포함합니다. 극지방의 커버리지는 위성 궤도와 기기 커버리지에 따라 제한됩니다. 이 컬렉션은 약 163,840m x 163,840m를 포함하는 이미지로 구성되며 각 이미지에는 64D 임베딩 공간의 각 축에 해당하는 64개의 밴드 {A00, A01, …, A63}가 있습니다. 모든 밴드는 삽입 공간의 64D 좌표를 집합적으로 참조하며 독립적으로 해석할 수 없으므로 다운스트림 분석에 사용해야 합니다. 모든 이미지는 UTM_ZONE 속성으로 표시된 대로 현지 Universal Transverse Mercator 투영으로 생성되며, 캘린더 연도를 반영하는 system:time_start 및 system:time_end 속성이 있습니다. 예를 들어 2021년의 삽입 이미지에는 system:start_time이 ee.Date('2021-01-01 00:00:00')과 같고 system:end_time이 ee.Date('2022-01-01 00:00:00')과 같습니다. 이러한 삽입은 단위 길이이므로 크기가 1이고 추가 정규화가 필요하지 않으며 단위 구체에 분산되어 있으므로 클러스터링 알고리즘 및 트리 기반 분류기와 함께 사용하기에 적합합니다. 임베딩 공간은 연도별로도 일관되며, 두 임베딩 벡터 간의 내적 또는 각도를 고려하여 여러 연도의 임베딩을 조건 변경 감지에 사용할 수 있습니다. 또한 임베딩은 선형으로 구성할 수 있도록 설계되었습니다. 즉, 더 거친 공간 해상도로 임베딩을 생성하기 위해 집계하거나 벡터 산술로 변환해도 의미론적 의미와 거리 관계를 유지할 수 있습니다. 이러한 삽입은 광학, 레이더, LiDAR 및 기타 소스를 비롯한 여러 소스를 동화하는 지리 공간 모델에 의해 생성됩니다 (Brown, Kazmierski, Pasquarella 외, 검토 중). 표현은 여러 센서와 이미지에서 학습되므로 표현을 삽입하면 클라우드, 스캔 라인, 센서 아티팩트, 누락된 데이터와 같은 일반적인 문제를 효과적으로 완화하여 분류, 회귀, 변화 감지 분석에서 다른 지구 관측 이미지 소스를 직접 대체할 수 있는 원활한 분석 준비 기능을 제공합니다. 일부 대규모 스와스 및 데이터 가용성 아티팩트가 눈에 띌 수 있지만 이는 일반적으로 사소한 벡터 오프셋을 나타내며 다운스트림 처리나 결과에 큰 영향을 미치지 않습니다.

Google Satellite Embedding データセットは、学習済みの地理空間エンベディングのグローバルな分析対応コレクションです。このデータセットの各 10 メートルのピクセルは、単一の暦年で、さまざまな地球観測機器とデータセットによって測定された、そのピクセルとその周辺の地表状態の時間的軌跡をエンコードする 64 次元の表現（「エンベディング ベクトル」）です。バンドが物理測定に対応する従来のスペクトル入力や指標とは異なり、エンベディングは、複数のソースのマルチモーダル観測間の関係を直接解釈しにくいがより強力な方法で要約する特徴ベクトルです。 このデータセットは、潮間帯やサンゴ礁帯、内陸水路、沿岸水路など、陸地の地表と浅瀬を対象としています。極域の観測範囲は、衛星の軌道と観測機器の観測範囲によって制限されます。 このコレクションは、約 163,840 メートル × 163,840 メートルの範囲をカバーする画像で構成されています。各画像には 64 個のバンド {A00, A01, …, A63} があり、64D エンベディング空間の各軸に対応しています。すべての帯域は、エンベディング空間の 64D 座標をまとめて参照し、個別に解釈できないため、ダウンストリーム分析に使用する必要があります。 すべての画像は、UTM_ZONE プロパティで示されるローカルのユニバーサル横メルカトル図法で生成され、エンベディングで要約された暦年を反映する system:time_start プロパティと system:time_end プロパティを持ちます。たとえば、2021 年のエンベディング画像には、system:start_time が ee.Date('2021-01-01 00:00:00') に等しく、system:end_time が ee.Date('2022-01-01 00:00:00') に等しい値が設定されます。 エンベディングは単位長です。つまり、大きさは 1 で、追加の正規化は必要ありません。また、単位球全体に分布しているため、クラスタリング アルゴリズムやツリーベースの分類子での使用に適しています。エンベディング空間は年ごとに一貫しており、異なる年のエンベディングは、2 つのエンベディング ベクトルのドット積または角度を考慮することで、条件の変化の検出に使用できます。さらに、エンベディングは線形合成可能になるように設計されています。つまり、エンベディングを統合して空間解像度の粗いエンベディングを生成したり、ベクトル演算で変換したりしても、意味的意味と距離関係を保持できます。 エンベディングは、光学、レーダー、LiDAR などの複数のデータストリームを統合する地理空間エンベディング モデルである AlphaEarth Foundations によって生成されます（Brown、Kazmierski、Pasquarella 他、審査中）。 表現は多くのセンサーと画像にわたって学習されるため、表現を埋め込むことで、雲、スキャンライン、センサー アーティファクト、欠損データなどの一般的な問題を効果的に軽減し、分類、回帰、変化検出分析で他の地球観測画像ソースに直接置き換えることができるシームレスな分析対応機能を提供します。大規模なスワスとデータ可用性のアーティファクトが目立つことがありますが、通常は小さなベクトル オフセットであり、ダウンストリーム処理や結果に大きな影響を与えることはありません。

This dataset provide a times series of daily multi-sensor composite fields of Sea Surface Temperature (SST) foundation at high resolution (HR) on a 0.10 x 0.10 degree grid (approximately 10 x 10 km) for the Global Ocean, every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L3S SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. This dataset is generated daily within a 24 delay and is therefore suitable for assimilation into operational models. It is produced in the frame of Copernicus Marine Service and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010) at: https://data.marine.copernicus.eu/product/SST_GLO_SST_L3S_NRT_OBSERVATIONS_010_010

This dataset provide a times series of daily multi-sensor composite fields of Sea Surface Temperature (SST) foundation at ultra high resolution (UHR) on a 0.02 x 0.02 degree grid (approximately 2 x 2 km) for the North-East Atlantic (European North West shelf, Iberia, Bay of Biscay, Irish Sea down to Canary upwelling), every 24 hours. Whereas along swath observation data essentially represent the skin or sub-skin SST, the L3S SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed. The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. The processing is described on Copernicus Marine Service [SST_ATL_PHY_L3S_NRT_010_037 dataset] and users can refer to the user manual and quality documents available there for more details. This dataset is generated daily within a 24 delay and is therefore suitable for assimilation into operational models. It is produced in the frame of Copernicus Marine Service and the data available through various tools and protocols with a simple user registration from this service (product identifier: SST_ATL_PHY_L3S_NRT_010_037) at: https://data.marine.copernicus.eu/product/SST_ATL_PHY_L3S_NRT_010_037/

مجموعة بيانات Google Satellite Embedding هي مجموعة عالمية جاهزة للتحليل من عمليات التضمين الجغرافية المكانية التي تم التعلّم منها. كل وحدة بكسل تبلغ مساحتها 10 أمتار في مجموعة البيانات هذه هي تمثيل بـ 64 بُعدًا، أو "متّجه تضمين"، يشفّر المسارات الزمنية لظروف السطح في وحدة البكسل هذه وحولها كما تم قياسها بواسطة أدوات ومجموعات بيانات مختلفة لمراقبة الأرض، على مدار …

Dataset Card for Dataset Name

We downloaded satellite images from Major-TOM, provided by the European Space Agency, filtered for Germany, and used our vectorisation engine to extract vector embeddings with one of the latest embedding model.

  Datasource Details

Value

Datasource Major-TOM/Core-S2L2A

Region box(5.98865807458, 47.3024876979, 15.0169958839, 54.983104153) (Covers whole of Germany)

Date Range ('2020-01-01', '2025-01-01')

Cloud Cover (0, 10)… See the full description on the dataset page: https://huggingface.co/datasets/quasara-io/Quasara-MajorTOM-DE-SigLIP.

This dataset provide a times series of daily multi-sensor composite fields of Sea Surface Temperature (SST) foundation at ultra high resolution (UHR) on a 0.02 x 0.02 degree grid (approximately 2 x 2 km) over North-East Brazil, every 24 hours.

Whereas along swath observation data essentially represent the skin or sub-skin SST, the L3S SST product is defined to represent the SST foundation (SSTfnd). SSTfnd is defined within GHRSST as the temperature at the base of the diurnal thermocline. It is so named because it represents the foundation temperature on which the diurnal thermocline develops during the day. SSTfnd changes only gradually along with the upper layer of the ocean, and by definition it is independent of skin SST fluctuations due to wind- and radiation-dependent diurnal stratification or skin layer response. It is therefore updated at intervals of 24 hrs. SSTfnd corresponds to the temperature of the upper mixed layer which is the part of the ocean represented by the top-most layer of grid cells in most numerical ocean models. It is never observed directly by satellites, but it comes closest to being detected by infrared and microwave radiometers during the night, when the previous day's diurnal stratification can be assumed to have decayed.

The processing combines the observations of multiple polar orbiting and geostationary satellites, embedding infrared of microwave radiometers. All these sources are intercalibrated with each other before merging. A ranking procedure is used to select the best sensor observation for each grid point. The processing is the same (minus the optimal interpolation step) as for the Atlantic Near Real Time (NRT) L3S dataset available on Copernicus Marine Service [SST_ATL_PHY_L3S_NRT_010_037 dataset] and users can refer to the user manual and quality documents available there for more details.

This dataset is generated daily within a 24 delay and is therefore suitable for assimilation into operational models.

The global space on-board computing platform market is projected to be valued at $2.5 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 8.5%, reaching approximately $5.6 billion by 2034.

Google Satellite Embedding डेटासेट, दुनिया भर के जियोस्पेशल डेटा का एक ऐसा कलेक्शन है जिसका विश्लेषण किया जा सकता है. इस डेटासेट में मौजूद हर 10 मीटर का पिक्सल, 64 डाइमेंशन वाला प्रज़ेंटेशन या "एम्बेडिंग वेक्टर" होता है. यह पिक्सल, धरती की निगरानी करने वाले अलग-अलग इंस्ट्रुमेंट और डेटासेट से मिले डेटा के आधार पर, उस पिक्सल और उसके आस-पास की सतह की स्थितियों की समय के साथ हुई गतिविधियों को कोड में बदलता है. यह डेटा …

The Telematics Control Unit (TCU) market is projected to be valued at USD 8 billion in 2024, driven by factors such as increasing consumer awareness and the rising prevalence of industry-specific trends. The market is expected to grow at a CAGR of 9.5%, reaching approximately USD 20 billion by 2034.

Global Embedded Systems market size earned around $101.37 Bn in 2023 and is expected to reach $189.53 Bn by 2032, with a projected CAGR of 7.2%.

Comparison of embedded Air-Coil with CubeSat magnetic rod available in the market.

Il set di dati Google Satellite Embedding è una raccolta globale e pronta per l'analisi di incorporamenti geospaziali appresi. Ogni pixel di 10 metri in questo set di dati è una rappresentazione a 64 dimensioni, o "vettore di incorporamento", che codifica le traiettorie temporali delle condizioni della superficie in corrispondenza e intorno a quel pixel misurate da vari strumenti e set di dati di osservazione della Terra, per un periodo di…

ABSTRACT: This work deals with the problem of the design of an autonomous redundant system for the attitude determination of nanosatellites. The system consists of a circuit board equipped with three microcontrollers, a magnetic field sensor in three axes, and complementary commercial-of-the-shelf (COTS) components and connectors to provide data signal exchange the vehicle on-board computer. The main goal of this system, named SDATF (of the acronym in Portuguese Sistema de Determinação de Atitude com Tolerância a Falhas), is to provide autonomously the vehicle attitude from the information collected from the onboard computer (OBC) and the magnetometer. The adopted attitude determination algorithm is based on the well-known QUEST method, and the goal of this system is to get accurate attitude computations to low-orbit CubeSat satellites using COTS electronic components, and to provide highly reliable data and high availability levels using fault tolerance tools to avoid the harmful consequences of spatial radiation and its faults known as single event upsets (SEU). The article presents the general design of the fault-tolerant systems and experimental tests using bit flip injection methodology.

O conjunto de dados de incorporação de satélite do Google é uma coleção global e pronta para análise de incorporações geoespaciais aprendidas. Cada pixel de 10 metros nesse conjunto de dados é uma representação de 64 dimensões, ou "vetor de incorporação", que codifica trajetórias temporais de condições da superfície no pixel e ao redor dele, medidas por vários instrumentos e conjuntos de dados de observação da Terra, ao longo de um único ano civil. Ao contrário das entradas e índices espectrais convencionais, em que as bandas refletem medições físicas, as incorporações são vetores de recursos que resumem relações em observações multimodais e de várias fontes de uma maneira menos diretamente interpretável, mas mais eficiente. O conjunto de dados abrange superfícies terrestres e águas rasas, incluindo zonas entre marés e de recifes, vias navegáveis interiores e costeiras. A cobertura nos polos é limitada pelas órbitas dos satélites e pela cobertura dos instrumentos. A coleção é composta de imagens que cobrem aproximadamente 163.840 metros por 163.840 metros, e cada imagem tem 64 bandas {A00, A01, …, A63}, uma para cada eixo do espaço de embedding 64D. Todas as bandas devem ser usadas para análise downstream, já que se referem coletivamente a uma coordenada 64D no espaço de incorporação e não podem ser interpretadas de forma independente. Todas as imagens são geradas na projeção local Universal Transversa de Mercator, conforme indicado pela propriedade UTM_ZONE, e têm propriedades system:time_start e system:time_end que refletem o ano civil resumido pelas incorporações. Por exemplo, uma imagem de incorporação de 2021 terá um system:start_time igual a ee.Date('2021-01-01 00:00:00') e um system:end_time igual a ee.Date('2022-01-01 00:00:00'). Os embeddings têm comprimento unitário, ou seja, magnitude 1, e não exigem normalização adicional. Eles são distribuídos na esfera unitária, o que os torna adequados para uso com algoritmos de clusterização e classificadores baseados em árvores. O espaço de embedding também é consistente ao longo dos anos, e embeddings de diferentes anos podem ser usados para detecção de mudanças de condição considerando o produto escalar ou o ângulo entre dois vetores de embedding. Além disso, os embeddings são projetados para serem linearmente combináveis, ou seja, podem ser agregados para produzir embeddings em resoluções espaciais mais grosseiras ou transformados com aritmética vetorial, mantendo o significado semântico e as relações de distância. Os embeddings são produzidos por um modelo geoespacial que assimila várias fontes, incluindo ópticas, de radar, LiDAR e outras (Brown, Kazmierski, Pasquarella et al., em revisão). Como as representações são aprendidas em vários sensores e imagens, a incorporação de representações mitiga problemas comuns, como nuvens, linhas de varredura, artefatos de sensor ou dados ausentes, fornecendo recursos contínuos prontos para análise que podem ser substituídos diretamente por outras fontes de imagens de observação da Terra em análises de classificação, regressão e detecção de mudanças. Embora alguns artefatos de grande escala e disponibilidade de dados possam ser perceptíveis, eles geralmente representam pequenos deslocamentos de vetores e não afetam significativamente o processamento ou os resultados downstream.